Designing Sustainable Smart Connected Communities using
Dynamic Spectrum Access via Band Selection
Vijay K. Shah

Shameek Bhattacharjee

Department of Computer Science
University of Kentucky, Lexington, USA
vijay.shah@uky.edu

Department of Computer Science
Missouri University of Science and Technology, Rolla, USA
shameek@mst.edu

Simone Silvestri

Sajal K. Das

Department of Computer Science
University of Kentucky, Lexington, USA
silvestri@cs.uky.edu

Department of Computer Science
Missouri University of Science and Technology, Rolla, USA
sdas@mst.edu

ABSTRACT
While designing communication systems for sustainable smart connected communities (SCCs), particularly in rural or non-urban
areas, energy efficiency is as important as quality of service (QoS).
However, simultaneously satisfying both metrics is extremely challenging due to varying demands posed by heterogeneous sensing
modalities, lack of dedicated infrastructure, and various sustainability constraints. While low-power short range technologies often failto achieve high QoS, the use of 3GPP technologies e.g., LTE, LTEA, GSM for connected communities will eventually face spectrum
scarcity and cross technology interference. In this regards, dynamic
spectrum access (DSA) offers a viable technology that overcomespolicy constraints and improves spectrum scarcity by spectrum
sharing. In this paper, we indeed show that harnessing DSA in thecontext of SCCs can achieve notable benefits in terms of energy efficiency. Specifically, we propose a novel architecture using a small
scale DSA enabled overlay network over legacy infrastructure that
improves end-to-end energy efficiency while guaranteeing QoS. The
underlying idea is to selectively exploit distinct electro-magnetic
characteristics of various bands with a goal to intelligently match
any message requirement with a suitable band, and thus determine
the optimal TTL constrained energy-efficient (TcE) path that enhances end-to-end energy efficiency and meets the TTL deadline.
We formulate a constrained optimization problem and propose adynamic programming approach for determining the optimal TcE
path for any given message. Compared to the homogeneous band
approaches that opportunistically access channels within a predetermined band, our study shows that the band selection approach
improves energy efficiency by almost 40% while preserving QoS.

CCS CONCEPTS•Networks → Cognitive radios; •Hardware → Energy distribution; Impact on the environment; Sensor applications andPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACM
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BuildSys ’17, Delft, Netherlands© 2017 ACM. 978-1-4503-5544-5/17/11. . . $15.00
DOI: 10.1145/3137133.3137153

deployments; Emerging technologies; •Computer systems organization → Embedded and cyber-physical systems;

KEYWORDS
Dynamic Spectrum Access; Energy Efficiency; Sustainable Smart
Communities; Band Selection; Green Communication; QoS
ACM Reference format:
Vijay K. Shah, Shameek Bhattacharjee, Simone Silvestri, and Sajal K. Das.
2017. Designing Sustainable Smart Connected Communities using Dynamic
Spectrum Access via Band Selection. In Proceedings of BuildSys ’17, Delft,
Netherlands, November 8–9, 2017, 10 pages.
DOI: 10.1145/3137133.3137153

1

INTRODUCTION

Ubiquitous connectivity is crucial for smart and connected communities (SCCs) – be it rural, sub-urban or urban. Such connectivity
enables gathering, dissemination, and analysis of valuable information on multiple sensing modalities, for efficient decision making
with a goal to improve quality and experience of human life [4].
To form such SCCs, huge communication infrastructure is needed
to offload data from hundreds of physical sensor blocks to their respective decision making units (e.g., a data center or cloud) usually
within certain time deadlines (a QoS metric). Such communities
may be formed in both urban and non-urban areas. For example, asensor block could be a remote rural farm equipped with moisturesensors, soil monitors, and wind sensors; or a smart home withtemperature/light sensor, and smart meters; whose data need to be
offloaded to a central utility for decision making.
However, realization of such huge communication infrastructure faces severe economic and sustainability challenges for bothrural (non-urban) and urban areas. First, it is not cost-effective forproviders to invest in dedicated infrastructure like base stations,access points with backhaul links, and spectrum licenses in nonurban areas due to low revenue returns compared to the high initialinvestment [15], [16]. Hence, sensor blocks planned in non-urban
areas are usually far away from the data center or the nearest backhaul link. Second, while certain real-time latency critical decisions
having a local area scope may use fog computing [3] (which requires dedicated infrastructure), many decisions need data overa wider area in the long run. Therefore, even in urban areas, thedata need to travel larger distances between the data center and thesensor blocks. Even if the existence of infrastructure is assumed (asBuildSys ’17, November 8–9, 2017, Delft, Netherlands
in urban areas), sending the collected data from large numbers of
sensor blocks are likely to burden the existing wired networks andin turn require deployment of more routers and switches, which
would require additional power and hence, exacerbates the problem
of energy sustainability.
To address the above challenges related to communication infrastructure costs in non-urban areas and burdening the wired networkin urban areas, SCCs could be realized through a network of fixed(or mobile) cyber-physical systems (e.g., road side units or RSUs,
private smart vehicles, public transport) connected via wirelesslinks [7], [8], without additional dedicated infrastructures. Such an
option is feasible for decision making in SCCs that do not have very
short real-time deadlines (termed as time-to-live, or TTL), such as
rural smart homes constituting smart meters and indoor air qualityor humidity sensors. As an example, smart meter data usually have
one hour TTL for billing cycle whereas other basic sensor data
may tolerate higher time delays, like 5-6 hours. Some generic ideas
pertaining to such a possibility has been discussed in [4]. Thus,
wireless spectrum becomes a critical resource for the success of
SCCs in both non-urban and urban communities.
Current wireless technologies for SCCs typically include (i) Low
Power Short Range (e.g. ZigBee, Bluetooth, WiFi, Low6PAN using
2.4 GHz) and (ii) 3GPP Standards (e.g., LTE, GSM, LTE-A using
1700-2100, 900 MHz). Low power technologies, although seemingly
energy-efficient, have limited transmission coverage [6]. Since thedata often travel larger distance, a large number of devices need to
be deployed to form a multi-hop network [10] to guarantee a highQoS (i.e., data offloading within TTL deadline). However, such a
communication network exacerbates the problem of both energy
efficiency and infrastructure cost.
On the other hand, although 3GPP technologies (dominant in urban areas) provide higher transmission coverage, they incur higher
energy expenditure [13] due to two major reasons. First, the coexistence of increasingly myriad devices in the same spectrum willcause cross technology interference, thus negatively impacting
the signal-to-noise ratio (SINR) requirement. Therefore, either increased power or more re-transmissions would be required (seeSec 4). Second, the homogeneity of legacy spectrum access policy
does not provide the flexibility to handle data traffic with heterogeneous demands (e.g., varying message sizes or TTL deadlines) that
may be generated from the same sensor block. Such homogeneityof the spectrum access plan triggers the disadvantage of resource
over-provisioning or under-provisioning, which again negatively
impacts the achievable energy efficiency (see Sec 4).
In the last decade, Dynamic Spectrum Access (DSA) [5] hasemerged as an attractive enabling paradigm that allows certain
wireless devices to opportunistically switch and access two or moreunoccupied bands (called whitespaces and grayspaces) originallylicensed for other services on the condition of non-interference tothe primary licensees or users. This provides an option to breakaway from the homogeneity of legacy spectrum access without
the provider having to buy a fixed spectrum license. Empty channels within each band are liable to be selected for the actual communication. Till date, whitespace and grayspace networking has
been allowed by policy in TV Band [23] [24], GSM Band [16], LTE
Band [26], and Citizen Broadband Radio Service (CBRS) Band [1].

Vijay K. Shah et al.
While the original purpose of DSA was to improve spectrum efficiency [12] and reduce cross technology interference, the factthat it allows switching among different bands could be used to improve the overall energy efficiency by opportunistically matching
the electro-magnetic (EM) properties of each band with different
sensing modalities’ requirements. To this end, a decade long bodyof DSA research focused on interference free channel assignment
given a particular band [27] [28], most of the energy efficiency
research has been limited to efficient channel sensing approaches
only [29]. However, with wide band spectrum analyzers and thenotion of spectrum access systems (that coordinates spectrum assignments) [33] becoming increasing available, in near future a DSA
end node is expected to have multiple choices for band selection.
In this paper, we show that apart from other factors, the band
type is an important factor that affects the network energy efficiency given the varying message and QoS requirements. First,
we propose an energy-efficient communication architecture for
SCCs using a small number of DSA enabled devices that form an
overlay network on top of the legacy infrastructure. The proposed
approach exploits the distinct electro-magnetic (EM) characteristics of different bands in order to intelligently match any message
requirement to a suitable band and, hence determines an optimal
TTL constrained energy-efficient (TcE) path that achieves the best
end-to-end energy efficiency and meets the TTL deadline. We formulate a constrained optimization problem for the determinationof TcE path for any given message requirement. We also propose apolynomial-time dynamic programming approach for solving the
optimization problem where the DSA overlay topology is relatively
steady. Compared to the homogeneous band access and spectrum
sharing approaches that are restricted to opportunistically accessan available channel within a predetermined band, we show thatthe proposed approach utilizing band selection significantly improves the energy efficiency by almost 40% while preserving the
QoS. Finally, we investigate some challenges of band selection fora variable overlay topology without global knowledge as a casestudy. To the best of our knowledge, no prior work investigated theenergy efficient communication through suitable band selection
for various message requirements. The benefit of this work is thatit provides an easy-to-deploy and low cost option to connect nonurban communities with improved energy efficiency. Moreover,it provides an alternative for improved energy-efficient communication network without burdening the wired infrastructures or
incurring any additional cost, for urban communities.
The rest of the paper is organized as follows. Section 2 reviews
the related work and Section 3 presents the proposed DSA enabled
overlay architecture for SCCs. Section 4 discusses the distinct EMcharacteristics of various bands that can be exploited to achieveenergy-efficiency. Section 5 presents the proposed band selection
approach for a steady DSA topology. Section 6 presents the simulation results, while Section 7 discusses challenges and roadmap
for band selection under variable DSA topology. Finally, Section 8
concludes the paper.

2

RELATED WORK

We review the related literature for both traditional (non-DSA)
and DSA/spectrum sharing approaches for smart connected communities using wireless cyber-physical systems. Several non-DSA

Designing Sustainable SCCs using DSA via Band Selection
wireless communication systems (e.g. DakNet [17], KioskNet [18])have been proposed to provide connectivity by utilizing buses andpublic service vehicles (equipped with computers and Wi-Fi radio) as mechanical backhaul that provides internet connectivity torural or remote communities. In contrast, JaldiMAC [20] utilized
long-range directional WiFi to form a wireless mesh network forproviding connectivity in sparsely populated areas. In [19], the deployment of low power GSM based Village Base Station (VBTS) was
proposed for rural telephony. In [21] and [22], the authors proposedconnectivity utilizing hand-held wireless devices for transient conditions. Google [25] introduced the network of balloons travelingon the edge of space, designed to extend Internet connectivity topeople in rural and remote areas. However, these approaches utilize standard wireless technologies, such as WiFi, WiMax, GSM
base station, VSAT, WLAN, wireless mesh, or a combination ofthem, in order to provide connectivity to the rural communities.
Since all these devices access a homogeneous band (usually ISM
or LTE), they suffer from limited coverage, under-provisioning,over-provisioning, and policy constraints as discussed later. Finally,
3GPP/GSM technology providers are also less motivated to invest
in expensive wireless spectrum licenses even after government
subsidies in developing countries [16].
Recently, the researchers have proposed the utilization of TV
whitespaces [23], [24] and GSM whitespaces [16] for rural connectivity. These approaches allow the secondary devices to dynamicallyutilize the whitespaces in fixed band (e.g., TV, GSM) for data communication. Some researchers [11] have also proposed cognitive
wireless sensor network (CWSN) where each sensor harnesses DSA
for energy-efficiency. However, the CWSN approach is impracticalfor community scale deployments for it requires every sensor node
to be DSA enabled, which is costly. Additionally, these works do
not discuss what band type is utilized. We show in the paper that restricting the SCC to one such band is not optimally energy-efficient.

3

PROPOSED ARCHITECTURE

This section presents key components, network model and communication mechanism for the proposed DSA overlay architecture.

Figure 1: Proposed Architecture

3.1

Key Components

Sensor Blocks : A sensor block is a small physical sub network (suchas a smart building, a crop field etc.) constituting heterogeneous
sensing agents. A sensing agent could be a sensor or a smart device
that collects different sensory or contextual data including, texts,
images, audio, video, etc. Such data may have different sizes, ranging from few Kbs (e.g., text) to several Mbs (e.g., images, videos) andvarying TTL deadlines of few hours (non-real time). As an example,

BuildSys ’17, November 8–9, 2017, Delft, Netherlands
a rural smart home may constitute temperature/light sensors andsmart meter. Recall a smart meter data usually have 1 hour TTL forbilling cycle whereas a sensor data on pollution levels, moisture,etc. may have a larger deadline upto 5-6 hours. Therefore, eachsensor block can be a source of various data, each with heterogeneous demands. Owing to the smaller size of the sensor block, it
can be assumed that the sensing agents within each block form a
multi-hop ad-hoc network of low-power short range legacy technologies that forwards its messages to a visiting consumer premise
equipment [14]. Unlike previous works [11], our work does not
require the sensing agents to be DSA enabled, thereby making itpractical and cost-effective.
Consumer Premise Equipment (CPE) : A CPE acts as a proxy between the sensor blocks and the DSA overlay network (explained
below). A CPE could be a flying drone or a moving vehicle, that
periodically collects various messages from a predetermined set of
sensor blocks (denoted by q ∈ Q, where Q is the set of CPEs). Thisobviates the need for a dedicated infrastructure, such as separate access points or DSA enabled sensing agents, at each sensor block. We
consider that each CPE has two wireless interfaces– a low-power
short range interface and a DSA interface to communicate with thesensor blocks and other DSA enabled devices, respectively.
Data Center : A data center is a base station with a back haul linkto the final decision making unit, where all the generated messages
need to be delivered for the computation and analysis. Each memberin the set of data centers (denoted by C) has DSA capability.
DSA Overlay Network : The DSA overlay network is a collection of
DSA enabled devices that acts as a bridge between the CPEs and thedata center. For example, fixed road side assistance units (RSU) [9],connected public transportation, smart vehicles, etc. Such a set of
DSA enabled devices (denoted by R) form a wireless DSA overlay
network, where each DSA enabled device opportunistically accesses
an unoccupied channel on a suitable band for communication. We
refrain from discussing the details of the mechanisms used for the
discovery and selection of unoccupied (non-interfered) channel
within a given band because such issues have been extensively
studied in the literature [5], [27], [28].
We assume that there exists a set S of bands where each band s ∈S has several (sub) channels. We have considered S = {TV, LTE, ISM,
CBRS}, however, it can be easily extended to any other unlicensed
or licensed bands where DSA is allowed. For example, millimeterbands ( 30 − 300 GHz) are being discussed for communication in
future 5G technologies [31], [32]. The details of operating frequencies for these bands are shown in Table 1. Hereafter, a node denotes
a DSA enabled device.
While the DSA network topology could be of two types, thefocus of this paper is the first one. The types are:
Steady network topology : In this case, the overlay nodes are either
fixed (i.e., RSUs) or have predictable mobility trajectories (such as
public buses, municipal vehicles). Each node shares its trajectoryand spectrum availability information with other nodes in the network via a dedicated common control channel or other synchronization techniques as discussed in the recent DSA standards [1], [2].
These mechanisms make it possible for each node to possess global
knowledge about the approximate geographical location and spectrum availability at every other node.

BuildSys ’17, November 8–9, 2017, Delft, Netherlands
Variable network topology : In this case, the DSA overlay nodes
may have variable (unpredictable) mobility pattern (e.g. a private
vehicle). Hence, a node cannot possess accurate global knowledgeof the network. However it may gather the local knowledge about
the neighboring nodes via the common control channel.

3.2

Network Model

We model the DSA overlay network topology as a directed graph(see Fig. 3) G = (V , S, E) where V = Q ∪ C ∪ R is the set of all nodes
i.e., CPEs, data centers and intermediate nodes, respectively; S is
the set of band types; and E ⊆ (V × V × S) is the set of all directed
links between any two nodes over common (free) channel in any(s)band type. ei j ∈ E is a directed link from node i to node j over aband s ∈ S. Hence, there may exist atmost |S | unique links between
any given node pair i, j ∈ V . We denote any message in G by
m < u, v, L,T > where u is the source, v is the destination, L is themessage size, andT is the TTL deadline. Now, for a given message m,(s)(s)
each link over band s is characterized by a tuple < w i j (L), tˆi j (L) >,denoting the energy and latency costs, respectively.

3.3

Communication Mechanism

As shown in Fig. 2, a CPE q ∈ Q periodically broadcasts a Hellopacket in the predetermined sensor blocks. On receiving the Hellopackets, 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 CPE q. The CPE, then broadcasts
the Sleep packet in the sensor block such that the sensing agentsswitch back to the energy-saving Sleep mode. Meanwhile, the CPE
selects a suitable energy-efficient band (say, s ∈ S). We discuss theband selection approach in Sections 5 and 7 for steady and variable
DSA overlay topologies, respectively.

Vijay K. Shah et al.
c (s) . The similar process continues at each intermediate node in thepath from the CPE to the intended data center.
To meet the objective of this work (i.e., end-to-end energy efficiency while not sacrificing the QoS), any message m < u, v, L,T >from a node u (e.g., CPE) has to be delivered to the data center v
in an energy-efficient manner while guaranteeing the TTL deadline. Therefore, in the rest of the paper, we primarily investigate
the determination of TTL constrained energy-efficient (TcE) path,p(u, v) through optimal band selection for any given message msuch that the end-to-end energy efficiency is maximized and the
TTL deadline is met.

4

TRADE-OFFS BETWEEN BAND SELECTION
AND ENERGY EFFICIENCY

In this section, we first present the energy consumption modelfor DSA overlay network. Then, we discuss how unique electromagnetic characteristics offered by various bands can be intelligently exploited to improve the energy efficiency of the DSA overlay
network for SCCs.

4.1

Energy Consumption Model

For DSA overlay network graph G, let a source node u transmitsa message m < u, v, L,T > to the destination node v, which is(s)
duv Euclidean distance apart. Suppose, Pi is the transmit power
at any node i over band s and γ (s) is the transmission coverage(s)achieved over a band s. Let Ri j be the effective bit rate achieved
from node i to node j over the band s. Recall that different bands
have different electro-magnetic (EM) characteristics that may resultin different allowable transmit power, coverage and effective bit
rates (explained later in this section).
Since the DSA overlay network is influenced by various EM
factors, such as operational frequency, bandwidth, path loss factor,
and interference, the energy consumption model must consider
all these factors. Now, given a path p(u, v) for the message m, theend-to-end energy consumption is given by:Õwuv (L) =w isj (L) =

(s )

Õ

(s )

Pi

(s )

× t i j (L)

(1)

(s )

e i j ∈p(u,v )e i j ∈p(u,v )(s)where ei j denotes an intermediate edge on path p(u, v). The mes(s)sage transmission time ti j (L) = L(s ) , defined as the time taken toRi jdeliver the entire message m from node i to node j over a band s i.e.,(s)along the edge ei j as explained in Section 4.3). Now, consideringthat every node is restricted to utilize the same band, termed thehomogeneous band access then, wuv (L) is given by
Figure 2: Communication Mechanism
Once the band s is determined, the CPE q shares the chosen bandand the channel information (denoted by c (s) ) with any node in
the DSA overlay network (or the data center if in range), whichthen switches to the channel c (s) in the chosen band type s. Notethat a unique node is determined in the steady topology whereas
in case of variable topology, the CPE q floods the information to
every neighboring node to improve the chances of message delivery.
After that, q transmits the stored messages to the chosen node(s) in
the DSA overlay network (or data center if in range) over channel

(s )

(s )

wuv (L) = Pu × t i j (L) × H(2)

H is the number of hops required to transfer the message fromnode u to node v, calculated as H = duv(s ) .
γ

4.2

Operating Frequency and Energy Efficiency

We know that the received signal strength at a receiver shouldmeet some reception threshold τ to decode a signal accurately. The
generalized Frii’s transmission equation provides the relationshipbetween the received power at receiver j and transmit power from

Designing Sustainable SCCs using DSA via Band Selection

BuildSys ’17, November 8–9, 2017, Delft, Netherlands
Table 1: Spectrum profiletransmitter i over any channel with a representative frequency 1
f (s) in band s,

α
(s )

Pj

(s )

= Pi G i G j

C
4π f (s ) d≥τSpectrum
54-216 MHz (VHF TV Band)
470-698 MHz (UHF TV Band)698-806 MHz (700 MHz Band/FirstNet)
700-800 MHz/1700-2100 MHz (LTE Band)902 - 928 MHz (ISM Band)
2.4 - 2.5 GHz (ISM Band)
3.5 - 3.7 GHz (CBRS Band)
5.7 - 5.8 GHz (ISM Band)(3)

where, 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(s)(s)
between i and j, Pi is the transmit power of i, and P j is theC )α be a constant.
received power at j. Now ϕ = G i G j ( 4π(s )Pi
, it is evident that for higher frequen(f (s ) d )α(s)cies, the transmit power Pi ought to be increased to maintain the(s)same power above the threshold, i.e., P j ≥ τ , even though the(s)Then, from P j=ϕdistance d is unchanged. The resultant increased transmit power
requires more energy consumption (refer to Eqs. 1 and 2). Alternatively, if the transmit power is not increased, then τ is only met
at a lower distance. Hence, more intermediate hops would be required to traverse the same distance d, leading to the additionaltransmission and reception operations at each intermediate hop
which causes higher energy expenditure.
Moreover, the lower frequencies have larger wavelengths yielding better obstacle and wall penetration capabilities. Hence, thelower frequencies are less error prone, thus reducing (re) transmission overheads and enabling better non line-of-sight connectivity.
Among the bands that allow DSA, TV and LTE bands have lower
frequency allocations than traditionally used unlicensed band (2.4
GHz). Hence, these bands should be more energy efficient.
However, lower frequencies come at a trade-off in the sense thatthey usually have much lesser bandwidth of 6 MHz as opposed
to LTE (upto 20 MHz), and CBRS (upto 40 MHz). Thus, for larger
message sizes, the message transmission time (refer to Eq. 6) will
be higher. Alongside, supporting high data rate applications arealso challenging for lower frequencies offering smaller bandwidths.
However, frequencies offering higher bandwidths will reduce thetransmission time for a given message size and save energy. An important research question, therefore, is that whether the reductionin the transmission time for higher frequencies offsets the gain inenergy efficiency due to reduced hop count.

4.3

Bandwidth and Energy Efficiency(s )Pd is =∫ f (s )
2

(s )

f1

Si (f (s ) )d f

(4)

(s)
(s)
where Si is the power spectral density, ∆f (s) = (f 2 − f 1 ) is(s)
the bandwidth of any channel in the band type s and Pr eq is thetotal power dissipated over band s. From Eq. 4, it is clear that achannel offering higher bandwidth causes larger power dissipation.
Given a fixed transmission time for any message, higher bandwidthchannels will consume more energy than the lower ones. The
typical bandwidths for various spectrum is listed in Table 1.
On contrary, from Shannon-Hartley theorem, the effective bit(s)rate (Ri j ) between transmitter i and receiver j over any channel inband s with representative frequency f (s) is given by:
1 For example, f = 2.4 GHz for the frequencies in the range 2.412 - 2.462 GHzsused by legacy WiFi devices in ISM Band

(s )





= B (s ) loд2 1 + ϕ

(s )

Pi
(s
)
α
(s
)
(f d ) N(5)

where B (s) is the bandwidth of any channel in band type s and
(s)

(s)

χi j = P j /N (s) is the ratio of the communication signal power tothe interference and noise (SINR) at the receiver. The rest of thenotations are the same as before.
For a given fixed message of L bits, the total transmission time iscalculated as follows:(s )t i j (L) =

L
(s )
Ri j

=

L



B (s ) loд2 1 + ϕ

(s )
Pi
(s
)
(f d )α N (s )(6)From Eq. 5, given that the SINR is unchanged, a higher bandwidthchannel would offer a higher effective bit rate, thereby decreasing
the required transmission time.
Hence, from the relationship between bandwidth and required
power (Eq. 4) and effective bit rate (Eq. 5), the higher the channel
bandwidth, the higher is the dissipated power but the lower isthe transmission time. Thus, the trade-off is whether to choose ahigh bandwidth channel that reduces the transmission time andincreases the dissipated power or vice-versa for any given message.

4.4

Interference and 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 (1700-2100 MHz) band. Therefore, from Eq. 5,
it is evident that 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 to maintain thesame SINR with a low transmit power, thus saving energy.

4.5

The relationship between bandwidth and dissipated power could
be understood through Rayleigh-Parseval Equation:



(s )

Ri j = B (s ) loд2 1 + χi j

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

Packet Size and Energy Efficiency

Packet sizes in 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). Hence, larger packet sizes can be employed to reduce
per-packet overheads such as headers and preambles, as shownin [13]. Moreover, the higher packet size would decrease the overall
message transmission time. Both these factors promises to improvethe energy-efficiency.

5

BAND SELECTION WITH GLOBALKNOWLEDGE

In this section, we discuss the proposed band selection approach forsteady network topology where each node has the global knowledgeof the network. The main idea is to intelligently match the message
m < u, v, L,T > to a suitable band at each intermediate node
between the source u and destination v so as to construct the TTLBuildSys ’17, November 8–9, 2017, Delft, Netherlands
constrained energy-efficient (TcE) path p(u, v). We first show thatthe greedy approaches for band selection do not always achieve
best energy-efficient path, p(u, v) that also meets the TTL deadline(i.e., optimal TcE path) for any given message.

Vijay K. Shah et al.
accessed by concurrent messages (should they end up choosing thesame band in the same geographical region); unless in the unlikely
event that the number of such messages exceeds the total number ofavailable channels. Moreover, there may be a small channel accessdelay due to channel bargaining within a selected band, which is
negligible in the range of micro seconds; therefore the optimization
problem ignores it.
The determination of an energy-efficient path for a message
m < u, v, L,T > can now be formally defined as follows: Given a(s)directed graph G = (V , S, E), an energy cost w i j (L) and a latency(s)cost tˆi j (L) to transmit a message m < u, v, L,T > along a link(s)
ei j ∈ E, a source node u ∈ V , a destination v ∈ V , the optimization
0 and e 1 denote links between nodes
Figure 3: Network Model. e 1212
1 and 2 over band type 0 and 1, respectively. Tuples < 1, 4 > and
0 and e 1 ,< 3, 2 > signify energy and latency costs across links e 1212respectively.

Claim 1: Any path p 0 (u, v), which is constructed by selecting
optimal (i.e., energy-efficient) band locally, among available bands ateach intermediate node, may not always yield the optimal TcE pathp(u, v) for any given message m < u, v, L,T >.
Proof: Let us consider the network (depicted in Fig. 3) where
nodes 1 and 4 are the source and destination, respectively, and amessage m with TTL deadline T = 5, initially at node 1. Then,the path constructed by choosing locally optimal band at eachintermediate node p 0 (1, 4) would be
0 , <1,4>e 12

1 −−−−−−−−−→ 2 6→ 4
Band 0

There exists no solution i.e., optimal TcE path p 0 (1, 4) that meets
the TTL, T = 5. Here, the optimal TcE path p(1, 4) is as follows:
0 ,
e 12

<2,3>

3 ,
e 34

<2,2>

1 −−−−−−−−−→ 3 −−−−−−−−−→ 4Band 0Band 3Hence, the greedy approach of selecting optimal band at eachintermediate node locally, do not always guarantee the constructionof optimal TcE path for any message.
Claim 2: Any path p 0 (u, v), which is constructed by selecting
the optimal (or energy-efficient) unique homogeneous band at eachintermediate node, may not always yield the optimal TcE path p(u, v)
for any message m < u, v, L,T >.
Proof: It can be proved easily as before (from above example
network as shown in Fig. 3).
We present and prove these claims to show that the constructionof optimal TcE path for any given message is not straightforward.
None of the above greedy approaches can always guarantee theconstruction of optimal TcE path. Hence, we formulate the following optimization problem and propose a dynamic programming
approach to find the optimal solution in polynomial time.

5.1

Problem Formulation

This section formulates a constrained optimization problem where
the objective is to determine the TTL constrained energy-efficient(TcE) path for any message m < u, v, L,T >.
Although we solve the optimization problem for a given messagem, it will also work well for concurrent messages. This is because aband is composed of several channels, that can be simultaneously

problem is:Õminp(u,v )∈P 0 (u,v )(s )w i, j (L)

(7)

(s )
e i j ∈p(u,v )where P 0 (u, v) ⊆ P(u, v) is the set of all paths from the source nodeu to the destination node v which meets the constraints:(i) SINR constraint : For the successful decoding of any message(s)m at j, the achieved SINR χi j at receiver j must be greater than orequal to the prespecified SINR threshold χth .
(s )χi j ≥ χt h(8)(ii) Power constraint : As per the FCC guidelines, the maximum
transmit power at any transmitter i must be less than or equal toa fixed value, the maximum EIRP (effective isotropically radiated(s)power) Pmax over any band s ∈ S.
(s )

Pi

(s )

≤ Pmax(9)(iii) Transmission coverage constraint : For successful data(s)communication over any intermediate link ei j ∈ p, the Euclidean
distance di j must be less than or equal to the transmission coverage
γ (s) achieved in the chosen band s.
γ (s ) ≥ d i j(10)(iv) Unique spectrum constraint : Any transmitter-receiver nodepair i, j ∈ V must tune to a common available channel in a uniqueband s ∈ S.
Õ
s ∈S

(s )

(s )

e i j ≤ 1,

∀e i j ∈ p(11)(v) TTL constraint : Any message must be delivered within its
time-to-live (TTL) from its source
node v.
 Í node u to destinationˆi(s)Hence the total latency cost
t
(L)
for
the
chosen(s )e ∈p(u,v) jij

path, i.e., the message transmission delay (ti j = L/Rsij ) (first part)and propagation delay (di j /C) (second part) and queuing delay (ϵ),
must be less than or equal to the TTL bound T , shown as follows.
(s)

Õ

L

(s )
R
(s )
e ∈p(u,v ) i j

+

di j
+ϵ ≤T
C

(12)

ij

where C is the spectrum propagation speed(usually speed of thelight). Note that the propagation and queuing delays are usually
negligible compared to the message transmission delay.

Designing Sustainable SCCs using DSA via Band Selection

5.2

BuildSys ’17, November 8–9, 2017, Delft, Netherlands

Proposed TcE Algorithm

Algorithm 1 Initialization

We propose a dynamic programming (DP) approach for the above
constrained optimization problem. The proposed algorithm intelligently matches the message m to an optimal (energy-efficient)band such that the total energy cost incurred along path p(u, v) is
minimized whereas the TTL and other constraints are met. The
proposed TcE algorithm is inspired from the well-known FloydWarshall algorithm for all pair shortest path algorithm.
Now, for a message m with L bits and TTL deadline, T at eachsource node i ∈ V over band s ∈ S, the graph G = (V , S, E) canbe represented by a three-dimensional adjacency matrix A : |V | ×|V | × |S |, defined as follows:< 0, 0 >,
i=j




 < w (s ) (L), t (s ) (L) >, i , j, and constraintsijij
A[i, j, s] =in Eqs. 8, 9, 10, and 11 are met < ∞, ∞ >otherwise


13:
14:

k is an intermediate node of p : p can be broken into two subpaths: one sub-path p1 from i to k and another sub-path p2 from k
to j. Since p is energy-efficient path, k can only appear once in p.
Therefore, k can not be an intermediate node of p1 or p2 . Moreover,by optimality principle (that states that the sub-paths of 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 the set {1, . . . k − 1}.
We define the sub problems used by the DP approach as follows.
(k,T )
Let w i jbe the energy cost of the energy-efficient path fromnode i to j that meets TTL, T and all the intermediate nodes are in
the set {1, 2, ..., k}. Now, we utilize above observations to define
the recursive relation between the solutions of sub-problems, andultimately lead to the solution of the overall problem.
Base case:
1. k = 0, There are no intermediate nodes in the path, hence foreach i, j ∈ V ,
(s )

(s )

mins ∈S A[i, j, s] → w i j (L), if ∃s ∈ S s.t t̂ i j ≤ T∞otherwise(k,0)

2. T = 0, there is no path, hence w i j= ∞, for each i, j ∈ V .

if

(14)

(s )

i , j and A[i][j][s] → t̂ i j (L) == t and(s )minECost > A[i][j][s] → w i j (L) then(13)

k is not an intermediate node of p : all the intermediate nodes ofpath p are in {1, . . . , k − 1}. Hence, the energy-efficient path from ito j with intermediate nodes {1, . . . , k} is also the energy-efficient
path from i to j with intermediate nodes in {1, . . . , k − 1}.

(10:

isV al id P at h = t r ue(s )W [i][j][t ] = A[i][j][s] → w i j (L)

11:
12:

An element in A[i, j, s] is an ordered pair < ., . > where the firstand second parts denote the energy and latency costs, respectively
for a message m along the link eisj from node i to node j over bands. The TcE algorithm is based on the notion of intermediate node,
which is defined as follows.
Definition 1 (Intermediate node): Given a path p =< 1, 2, ..., (|l |−1), |l | >, an intermediate node is any node i such that i = 2, ..., (|l | −1), i.e., any node except 1 and |l |.
The TcE algorithm is based on the following observation. Let
us consider a subset of nodes {1, . . . k }. For any node pair i, j ∈ V ,let p be the path with least energy cost, among all paths between
i and j whose intermediate nodes are in {1, . . . k}. There are two
possible cases.

(0,T )
wi j=

1: Input: Adjacency matrix (W ), TcE path matrix (P )2: Ouput: W (0,T ) , P (0,T )3: for i = 0 to |V | do4:
for j = 0 to |V | do
5:minECost = ∞, isV al id P at h = f al se6:
for t = 0 to T do
7:
for s = 0 to |S | do
8:
if i == j then9:
W [i][j][t ] = 0, P [i][j][t ] = −1

15:
16:
17:
18:
19:
20:
21:

(s )

minECost = A[i][j][s] → w i j (L)P [i][j][t ] = i
if i , j and isV al id P at h == f al se thenW [i][j][t ] = ∞P [i][j][t ] = −1
if t > 0 and W [i][j][t ] > W [i][j][t − 1] thenW [i][j][t ] = W [i][j][t − 1]minECost = W [i][j][t − 1]
P [i][j][t ] = i

Inductive case, k > 0 and T > 0, we can select a path withthe least energy cost either using k as an intermediate node or not,however meets the TTL deadline, T .
(k,T )(k −1),t(k −1),t 1(k −1),(t −t 1 ) 
wi j= min w i j, min w ik
+ wk jt =1...Tt 1 =0...t

When k = |V | and t = T , then we have the final solution, whichis the total energy cost for the energy-efficient path for each pairof source-destination node pair with deadline T , i.e., W (|V |,T ) =(|V |,T )(w i j) for all i, j ∈ V .
Algorithm 2 TcE Path via Optimal Band Selection
1: Input: Adjacency matrix W(|V |,T )2: Ouput: TcE path p(i, j) and optimal energy cost w i j∀ i, j ∈ V

3: procedure TcE()
4:
W (0,T ) = initializeAdjacencyMatrix(W )5://Determine TTL constrained energy-efficient path for each node pairi, j ∈ V
for k = 0 to |V | dofor t = 0 to T do
for i = 0 to |V | dofor j = 0 to |V | dofor t 1 = 0 to t do
if W [i][j][t ] > W [i][k][t 1 ] + W [k ][j][t − t 1 ]then12:
W [i][j][t ] = W [i][k ][t 1 ] + W [k][j][t − t 1 ]13:
P [i][j][t ] = P [k ][j][t − t 1]
14:
Recursively navigate P [i][j][T ] to get the optimal path p(i, j) with
TTL deadline T .
6:
7:
8:
9:
10:
11:

Algorithm Description: The TcE algorithm uses a series of threedimensional matrices W (k,t ) for k = 0, . . . |V | and t = 0, . . . T .
(k,t )
Note W (k,t ) contains the elements w i j , that is the TTL (1 ≤t ≤ T ) constrained energy-efficient path between i and j using theBuildSys ’17, November 8–9, 2017, Delft, Netherlands

Vijay K. Shah et al.

intermediate nodes in {1, . . . k}. Also, we keep track of the optimal(k,t )
TcE path in another matrix Pi j . The TcE algorithm has two steps:
Step 1 (Initialization): The TcE algorithm first initializes the matrix
W (0,T ) to A under the following conditions (depicted in Algorithm1). It also initializes TcE path tracking matrix, P (0,T ) (Lines 8 - 21).
(0, t )

wi j

=



0 if
i == jmin w (0, t −1), min



(s )
s ∈S (A[i, j, s] → w i j (L))(s )if i , j and A[i, j, s] → t̂ i j (L) == t

ij






∞


(15)

ot her wise

Step 2 (TcE path via band selection): After the initialization (i.e., Step1), the TcE 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 , asshown in Algorithm 2. Also, the path tracking matrix P is updated
recursively (Lines 6 - 13).
Finally, any source node i delivers the message to the intended
destination node j via the computed optimal TcE path p(i, j) obtained from path tracking matrix P. The total time complexity of
the TcE algorithm is O(|V | 3 ×T 2 ) where |V | is the number of nodes,
and T is the message TTL deadline.

5.3

Discussion on Cost Benefit Analysis

Many detractors of sustainability argue that green computing incurs higher capital expenditure and operational cost which drains
the economy/revenue and makes such designs infeasible in practice.
However, we believe that our proposed approach of opportunistic
band selection to realize SCCs, is free from most such disadvantages due to the following reasons: (i) The use of DSA enabled
access does not require a SCC provider to own dedicated spectrum
licenses, worth millions of dollars regardless of variable traffic volume; (ii) Multiple smart communities can be connected with a small
set of DSA overlay nodes where each DSA node average cost is
roughly around $500-$700. In contrast, our design obviates theneed for new dedicated infrastructure such as cellular base station($200, 000/station) [30] in rural areas, and the need for extensive
wire line communications to increase the available bandwidth inurban areas. Also burdening the existing networks, whether wired
or wireless is obviated; and (iii) Some real examples of micro-telco
models of DSA based deployment has already taken place in relatively rural areas in the developing world such as Papua [16], South
Africa [34], Mexico [35], and Philippines [36].
To summarize, there is sufficient practical evidence to suggestthat the equipment and operational costs are far less comparedto the traditional approaches. The major hurdles are the policy
issues which surround spectrum planning. However, significant
breakthroughs [1], [2] in spectrum policy have been made in recent
years and DSA enabled devices mounted over drones, smart public
transport [7], and private smart cars [8] are going to be a reality.

6

SIMULATION RESULTS

In this section, we evaluate the proposed band selection approachagainst homogeneous band access approaches, for steady DSA
overlay topology in terms of the following performance metrics:(i) Energy Efficiency - the average amount of energy consumed totransfer a generated message from a sensor block to the intended
data center, (ii) Message Delivery Ratio (MDR) - the fraction of total
messages delivered to the data center within the TTL deadline, tothe total generated messages at all sensor blocks, and (iii) Network
Latency - the average delay incurred (i.e., total transmission delay,
propagation delay and queuing delay at each intermediate node) indelivering all messages, each from its block to the data center.
Each of the above performance metrics is evaluated against thefollowing parameters: (a) varying message size, (b) varying TTL
deadline, and (c) varying source-destination pair distance. The firsttwo parameters represent the heterogeneity in various messages,
while the third one represents the heterogeneity in geographicaldistance between the sensor blocks and data centers.
Simulation Settings: We develop a Java simulator to simulate a
SCC scenario with 5 data centers, 15 CPEs, 30 sensor blocks, eachwith 50-100 sensing agents. On average, each CPE periodically visits
2 CPEs with a round trip time of 1 hour. The steady DSA overlay
topology consists of 200 DSA nodes (e.g., public buses, RSUs etc.).
Now 50% of overlay nodes (imitating RSUs) are randomly placed
and are static whereas other overlay nodes (imitating public buses)
move over a fixed trajectory. We consider that each DSA enabled
node has the knowledge of location and spectrum availability at
every other node in the network. Unless otherwise stated, themessage size, TTL deadline and farthest distance between sensor
blocks and data centers are taken as 250 MB, 6 hours, and 45 Km
for the experiments, respectively.
Each band has a FCC mandated maximum allowable transmit
power which can not be exceeded by any node operating on a DSA
basis. Hence, we consider a transmit power (1 W (30 dBm)) which isallowable on a secondary basis, in all ISM, TV, LTE and CBRS bands.
The values of other controlling parameters are as follows: path loss
factor α = 3.5 (sub-urban area), SINR = −75 dBm (3.16 × 10−8 mW),
and received power threshold (τ ) = −20 dBm (0.01 mW).

6.1

Energy Efficiency

Figs. 4(a), 4(b), and 4(c) clearly show that the energy consumed
by our proposed approach is significantly lower than all homogeneous band approaches across various message sizes, TTL deadlines
and source-destination distances. The average amount of energy
saved by our approach is 36.2%, 47%, and 42.2% against all otherapproaches combined (whereas 15%, 20% and 13% against the bestone), for various message sizes, TTL deadlines and distances, respectively. This is because our approach selects a suitable band at eachintermediate node for enhancing energy efficiency unlike scenarios
where the node is restricted to access only one predetermined band.
Note that in Fig. 4(c), the energy consumed is 0 J for distance
75Km for ISM and CBRS bands. This is because there existed nointermediate nodes in the communication range, that could forward
the message from the sensor block towards the data center. Hence,no energy was consumed at any node in the DSA overlay network. However, the message delivery ratio is negligible (≈ 0%) (Fig.
5(c)), hence defeating the purpose of SCC. The proposed approachachieves the highest energy efficiency, yet satisfying QoS.

6.2

Message Delivery Ratio

As illustrated in Figs. 5(a), 5(b), and 5(c), our approach yields either
comparable or better message delivery ratio (MDR) compared tothat of homogeneous band approaches, irrespective of varying message sizes, TTL deadlines and distances between the sensor blockand data center, respectively. The improvement in MDR for ourapproach is almost 15% for message size (> 500Mb), 17% for TTL

Designing Sustainable SCCs using DSA via Band Selection

Proposed
ISM
CBRS
LTE
TV

10

100

250

500 1000

Message Size (in MB)

160

Energy Consumed (in J)

100
90
80
70
60
50
40
30
20
10

Energy Consumed (in J)

Energy Consumed (in J)

200
180
160
140
120
100
80
60
40
20
0

BuildSys ’17, November 8–9, 2017, Delft, Netherlands

Proposed
ISM
CBRS
LTE
TV

1

2

140
120
100

3

4

5

6

Proposed
ISM
CBRS
LTE
TV

80
60
40
20

15

TTL deadline (in hours)

(a)

30

45

75

Distance (in Km)

(b)

(c)

1
0.8
0.6
0.4
0.2
0
10

Proposed
ISM
CBRS
LTE
TV

100
250
500
Message Size (in MB)

Message Delivery Ratio

Message Delivery Ratio

Message Delivery Ratio

Figure 4: Energy Efficiency: (a) Message Size, (b) TTL, and (c) Distance between sensor block and data center
1
0.8
0.6
Proposed
ISM
CBRS
LTE
TV

0.4
0.2

1000

0
1

2
3
4
5
TTL deadline (in hours)

(a)

1
0.8
0.6
0.4
0.2

6

0
15

Proposed
ISM
CBRS
LTE
TV

30
45
Distance (in Km)

(b)

75

(c)

5
4

Proposed
ISM
CBRS
LTE
TV

3
2
1
0
10

100
250
500
Message Size (in MB)

1000

6
5
4

Proposed
ISM
CBRS
LTE
TV

3
2
1
0
1

2
3
4
5
TTL deadline (in hours)

(a)

(b)

6

Network Latency (in hours)

6

Network Latency (in hours)

Network Latency (in hours)

Figure 5: Message Delivery Ratio: (a) Message Size, (b) TTL, and (c) Distance between sensor block and data center
654

Proposed
ISM
CBRS
LTE
TV

3
2
1
0
15

30
45
Distance (in Km)

75

(c)

Figure 6: Network Latency: (a) Message Size, (b) TTL, and (c) Distance between sensor block and data center
deadline (< 3 hours) and 10% for distance (> 50 Km), compared tothe best of homogeneous bands, i.e., LTE Band for first two cases
and TV Band for the third one. This is because our proposed TcE
algorithm simultaneously accounts for TTL besides enhancing theenergy savings. Hence, the proposed approach always guarantees
the highest MDR for any message with varying heterogeneity demand. However, the homogeneous band approach do not have theflexibility of choosing any other band than the pre-specified one,
thereby incurring poor MDR.

6.3

Network Latency

Figs. 6(a), 6(b), and 6(c) show that the proposed approach achieves
better network latency compared to other approaches, irrespectiveof message and geographical heterogeneity. This is because ourapproach attempts to choose the path with least message transmission time while determining the TcE path (from Eq. 2). Specifically,
our approach improves the network latency by almost 16%, 23% and
14% compared to the best of homogeneous band approaches (i.e.,LTE), for varying message sizes, TTL deadlines and geographicaldistances, respectively.
Again note that the network latency for ISM and CBRS bands
are not shown for distance = 75 Km (see Fig. 6(c)). This is because
their network latency is equivalent to infinity as no messages weresuccessfully delivered to their intended data centers, within their
TTL deadlines (see Fig. 5(c)).

7

VARIABLE DSA TOPOLOGYTo realize the true power of ubiquitous connectivity, we believe
the private smart vehicles of the future could also be harnessed as
members of the DSA overlay network. However, since mobility insuch cases is not controlled due to variables routes, constructing
a global knowledge might be impossible. We investigate certain
challenges and a road map for energy efficient band selection undersuch 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 >, hopefully leading to the end-to-end energy
efficiency while meeting the TTL deadline.
The greedy algorithm selects the band s ∈ S at any given node u
for any message m only if the following conditions are met: (i) if thenext hop is the destination node, or (ii) the energy consumed overselected band s is the least, the latency cost is less than the TTL andthere exists at least two nodes in the communication range overthe selected band s. These conditions are important to improve thechances of successful data delivery to the destination node. Oncethe suitable energy-efficient band s is selected at the current node

BuildSys ’17, November 8–9, 2017, Delft, Netherlands

Vijay K. Shah et al.

300
250
200

Proposed
ISM
CBRS
LTE
CBRS

150
100
50
0

1

10

100

Message Size (in MB)(a)

Message delivery ratio

Energy Consumed(kJ)u, it floods the messages over the selected band s to all neighboring
nodes that lie in the communication range. The time complexity of
the algorithm is O(|V | × |S |) where |V | and |S | are the total numberof nodes and band types, respectively.
1

TV
LTE

Proposed
WiFi

CBRS

REFERENCES

0.8
0.6
0.4
0.2
0

1

10

100

Message size (in MB)(b)

Figure 7: Variable DSA Overlay Network: (a) Energy Efficiency, and (b) Message Delivery Ratio
Challenges and Future Roadmap : Let us investigate the performanceof the greedy local optimization in variable DSA topology, and draw
key challenges and a future roadmap for further research. From Fig.
7(b), it is evident that the proposed greedy approach outperforms
others in terms of MDR, however, it suffers in terms of energy
efficiency compared to that of other homogeneous band accessapproaches (see Fig. 7(a)). This is mainly due to various contextual
factors, such as unpredictable node mobility patterns, node contact
behaviors, etc. The results are similar for other two parameters(plots not shown here for lack of space).
We conclude that a locally optimized greedy solution alone is
not an apt solution to improve the end-to-end energy efficiency via
dynamic band selection. In fact, the optimization under that caseshould be complemented by an efficient delay tolerant routing protocol that reduces the unpredictability of determining the best setof intermediate nodes by learning geographical contextual factors
and contact mobility patterns. Therefore, we plan to investigate
the contextual factors and come up with a spectrum and mobility
aware approach that improves the end-to-end energy efficiency
for any given message that also meets the TTL deadline in such achallenging variable DSA topology scenario.

8

We would also like to acknowledge Rishab Singhal for his assistancewith the simulation experiments. Sajal K. Das is also a distinguishedvisiting professor at Zhejiang Gongshang University, Hangzhou,
China.

CONCLUSION

In this paper, we proposed a novel architecture that uses a small
number of DSA enabled devices for forming a DSA overlay network
on top of the legacy infrastructure. The goal is to enhance end-toend energy efficiency while guaranteeing QoS. We discussed how
EM characteristics can be exploited for intelligent matching of any
message requirement to a suitable band. We also formulated a constrained optimization problem and proposed a dynamic programming approach for determining TTL constrained energy-efficient
path for any given message with heterogeneous requirement. Compared to the homogeneous band access approaches, the proposed
approach excels in terms of energy efficiency while satisfying QoS,
irrespective of message and geographical heterogeneity, and thus
bypassing under- and over-provisioning issues.

ACKNOWLEDGMENTS
This research is partially supported by the NSF grants CNS-1545037,
CNS-1545050, and NATO Science for Peace and Security grantG4936, ”Hybrid sensor networks for emergency critical scenarios”.

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