Wireless Power Transmission in Smart Cities: The wIshood

Wireless Smart Neighborhood

Genaro Longoria, Fayaz Akhtar and Lei Shi

Telecommunications Systems and Software Group, Waterford Institute of Technology, Carriganore, Co. Waterford, Ireland

Keywords:

Control Engineering, Scheduling, Smart City, Smart Neighborhood, Wireless Power Transmission, wIshood.

Abstract:

Wireless power transmission (WPT) of scale is the next step in power electronics. In this paper, we propose

the Wireless Smart Neighborhood (wIshood). The idea presented serves smart city planners and developers to

consider the future societal impacts of current and expected technological advancement. The wIshood merges

ICT, IoT, CC, SDN and WPT to propose a solution to foster the creation and growth of the building blocks of

modern societies. We outline the architecture and challenges of wireless smart neighborhoods. The wIshood

is a solution to electricity congestion and deployment costs of transmission and distribution infrastructure.

1 INTRODUCTION

In the recent past, city planners have been busy

resolving the best trade-off among mobility, green

zones and residential and commercial expansion. To

address these conﬂict-of-interest problems, techno-

logical breakthroughs will be fundamental for fu-

ture smart city planning. At a careful but steady

pace, modern cities are embracing the information

and communication developments. Products and ser-

vices, from technological innovations, will become

ubiquitous in future smart cities. From rural to ur-

ban, industrial to residential and the overlap, Wireless

Power Transmission (WPT), the Internet of things

(IoT) and Information and Communication Technolo-

gies (ICT) will become a cornerstone in the design of

new and growth of human settlements.

The evolution towards smart environments is be-

ing welcomed by society. Nowadays it is com-

monplace for cities to be equipped with free WiFi

hotspots, real-time trafﬁc information and safety

surveillance, to mention a few. In this respect, cre-

ativity driven and farsighted governments are playing

a crucial role to speedup the technological evolution

of the city. For example, Pervasive Nation, a pub-

lic funded initiative, is empowering academia and en-

trepreneurs to develop and implement an IoT testbed

of scale in Dublin city (PervasiveNation, 2016). With

the ever-increasing services and cloud connectivity,

IoT devices are set to pervade all aspects of our daily

lives. Thereby revolutionizing a broad range of appli-

cations in a variety of domains, such as healthcare,

home automation, transportation, intelligent energy

management and smart grids (Bellavista et al., 2013).

Neighborhoods form an important building block

of every city. Nevertheless, presently they have a

passive rather than active role in the progress of the

city. In a top-down manner, technology is percolating

into neighborhoods. In smart cities, legislation is re-

quiring a change of old practices towards an efﬁcient

use of resources. Nevertheless, electricity distribution

still relies on cables for its delivery.

In this paper we propose the wireless smart neigh-

borhood: The wIshood

. The novelty of the wIs-

hood is that households use WPT for electricity sup-

ply. The energy is wirelessly supplied from a local

renewable power station (RPS). Although, still in an

early stage, wireless power transmission is gaining

momentum. Both, industry and academia know that

WPT will be the solution to a variety of problems.

With WPT, the wIshood has three major advantages

to positively contribute to the smart city. Firstly, the

increase of renewable electricity integration decreases

fossil-fuels dependence. Secondly, city growth will

have a lesser impact on the distribution and trans-

mission capacity. Lastly, the wIshood will promote

industrial investment by reducing transmission con-

gestions hence lowering marginal energy prices. The

wIshood exploits the edge cloud paradigm. The dis-

tributed architecture supports heterogeneous IoT de-

vices, scheduling, information, processing and con-

trol of energy supply and demand for households.

Pronounced as wiz-hood

Longoria, G., Akhtar, F. and Shi, L.

Wireless Power Transmission in Smart Cities: The wIshood - Wireless Smart Neighborhood.

DOI: 10.5220/0006364003170322

In Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2017), pages 317-322

ISBN: 978-989-758-241-7

317

The reminder of the paper is organized as follows.

In section 2, we do a brief outline of related litera-

ture. In section 3, we present the architecture of the

wIshood. In section 4, we outline the challenges to be

addressed by the research community. Lastly, section

5 summarizes the work presented.

2 RELATED LITERATURE

The Smart city is a green ﬁeld for research. Although,

there are ingenious attempts of materializing some of

the conceptual designs, technological progress is con-

stantly and at a faster pace widening the possibilities.

For the reader interested in a survey of Smart City ar-

chitectures, Kyriazopoulou recently presented a thor-

ough literature review on the topic (Kyriazopoulou,

2015).

The work of Akcin et al., describes passive and

active solutions to problems associated with popu-

lation expansion and urbanization. Among the ac-

tive methods, they comment on improving trafﬁc ﬂow

with road-side sensors. On the passive approach, e.g.,

they presented a Swedish study on natural ventilation

of cities to reduce the power for cooling buildings

(Akcin et al., 2016).

Reducing the peak-to-average ratio (PAR), hence

balancing the load curve, is one of the main goals of

demand side management (Cakmak and Altas, 2016),

(Yoon et al., 2014), (Liu et al., 2014), (Zhu et al.,

2015). For instance, Cakmak and Altas, developed an

Cuckoo search algorithm (CSA) to address the prob-

lem of appliance scheduling in a neighborhood. The

approach is oriented to increase the efﬁciency of elec-

tricity supply and demand. The algorithm minimizes

the objective function of the tradeoff of shiftable loads

scheduling and consumer satisfaction by means of ﬁ-

nancial beneﬁts. They showed the CSA scheduling

reduced the PAR from 3.27 to 2.53 (Cakmak and Al-

tas, 2016).

Smart metering of electricity, in smart homes, was

described in the work of Pingle et al. They used an

Arduino mote to gather data from the IoT equipped

appliance. The raw data, in amperes, was processed in

the cloud to output watts and ﬁnally sent to the user’s

mobile phone. They commented on the implications

and advantages for the end user of real time informa-

tion on energy bill savings (Pingle et al., 2016).

Presently, the four most common technologies for

wireless power transmission are: 1. Electromagnetic

radiation; 2. Inductive coupling; 3. Magnetic resonant

coupling; and 4. Acoustic waves (Shinohara, 2014).

Antennas alignment is one of the major concerns in

WPT applications. A Planar Archimedean Coil was

COMMUNICATION

SDN, CRN, Encrypted Tunneling, IPsec, MQTT protocol

INFORMATION

Data Integrity, Security, Data Mining and Analytics

CONTROL

Coordination, Safety

PROCESSING

Energy Analytics, Fair Distribution

SCHEDULING

Load Smoothing, GUI

Figure 1: Layered architecture of the wIshood.

proposed to overcome misalignment between trans-

mitter and receiver (Feenaghty and Dahle, 2016).

Imura et al., summarized the WPT requirements for

electric vehicles (EV) charging in Japan. They de-

scribed a road infrastructure for WPT to provide a so-

lution to the problem of long-distance traveling with

EV (Imura et al., 2016). Recently, Jian et al., pre-

sented a proof of concept of WPT with inductive cou-

pling. In their laboratory setup, they wireless trans-

fered electricity from a renewable source to a load

with a pivoting antenna (Jian et al., 2016). Although,

long distance WPT over free space is feasible (Ma

et al., 2016), WPT over long distances among obsta-

cle rich environments is currently a topic of research.

3 ARCHITECTURE

The layered architecture of the wIshood is shown in

Fig. 1. It is composed of ﬁve layers: scheduling,

information, communication, processing and control.

Each household has IoT deployments of metering

sensors, actuators, appliances and power switches.

The functions of the IoT devices is to provide the

hardware for data collection and communication. Ma-

chine learning and control theory will serve as the

foundations of the Processing layer (Tobar et al.,

2014). Finally, the control layer manages the energy

distribution infrastructure; which is composed of the

RPS transmitter antenna and the households’ receiver

antennas.

3.1 Communication

The fundamental task of the Communication layer

is to ensure that the two way transfer of data from

heterogeneous IoT motes and the edge-cloud is done

in an efﬁcient and secure way. We propose a Soft-

ware Deﬁned Network (SDN) and Cognitive Radio

SMARTGREENS 2017 - 6th International Conference on Smart Cities and Green ICT Systems

318

Figure 2: Communications are between IoT to gateways,

gateways to edge-clouds and between edge-clouds.

Networks (Khan et al., 2016) to manage and trans-

fer data from gateways (e.g., switches and routers) at

each household to the edge-cloud; where the process-

ing and decision making takes place.

The control of the queueing networks is done with

a Lyapunov optimization algorithm (Samarakoon

et al., 2016). To account for bandwidth bottlenecks

and latency we adopt the Message Queue Telemetry

Transport protocol (Jo and Jin, 2015). Figure 2, rep-

resents a high level communication view of the ar-

chitecture. The data generated by the smart houses,

RPS, weather forecast module and storage is chan-

nelled through gateways to the edge-cloud. IPsec tun-

neling is the cryptographic protocol of the communi-

cations network.

3.2 Information

The acquisition of the sheer amount of high-speed

data, constantly generated from smart homes, is a sig-

niﬁcant task upon storage and analysis (Beckel et al.,

2014). The Information layer resides in the edge-

cloud, we adopt the integrated IoT Big Data Analytics

framework (Bashir and Gill, 2016).

The principal functionality is to make the data ac-

cessible to the Scheduling, Processing and Control

layers. A major task is to assure data integrity and

security. At this layer, a ﬁrst phase of data mining is

implemented to eliminate redundant and non useful

values. Thus, reducing the strain upon and bandwidth

required by the Communication layer.

The stored data is fetched by the algorithms in the

Processing phase. Figure 3, shows the ﬂow of infor-

mation among the IoT devices, cloud, Processing and

Control layers. Dashed lines symbolize the WSN; red

lines, power transmission and black lines, wired com-

munication.

3.3 Scheduling

The scheduling layer positively exploits the ﬂexibil-

ity of load shifting. The work of Liu et al., catego-

rized appliances as: 1. Shiftable; 2. Throttleable; and

3. Essential (Liu et al., 2014). Appliances such as

dishwashers and laundry machines can be assigned

a time slot to run. HVAC (heating, ventilation and

air condition), although have rigid operation peri-

ods, are ﬂexible to power adjustments within pre-

deﬁned ranges. A graphical user interface (GUI) is

implemented for individuals to submit their desired

scheduling of shiftable and operation ranges for throt-

tleable devices. The output of the Scheduling layer

is sent to the Processing layer (see §3.4). The latter

analyzes the available resources and the energy de-

mand. In case of mismatches, alternative scheduling

arrangements are feedback to the households.

3.4 Processing

The processing layer, addresses the competing ne-

cessities of each household, proposes alternative sce-

narios to conﬂict-of-interest problems and determines

tradeoff solutions between divergent goals. The func-

tions of this layer are to perform the energy analytics

and provide feedback when supply cannot meet de-

mand. We use a ”divide and conquer” methodology

to approach the non-linearity, uncertainty and highly

coupled interactions in the wIshood.

The input, is the data from the information layer.

Feedback is sent back to the scheduling layer. The

processing unit integrates demand side management,

energy generation and storage to optimize energy dis-

patch to the neighborhood. This layer provides a so-

lution to the task of fair distribution of a scarce re-

source in a heterogeneous demand environment. To

address this challenge, the functionalities of the pro-

cessing layer include machine learning, optimization

and forecasting algorithms.

A Kohonen self-organized network is used to re-

duces the dimensionality of the data. Then a Hidden

Markov Model serves to classify the massive amount

of sensor data; to be gathered and transfered by the

IoT infrastructure. The HMM function is to deter-

mine clusters and patterns in the data. The output of

the HMM is sent to the optimization module (OM).

The functions of the OM, are twofold: 1. Mini-

mize the cost function of the wIshood energy distri-

bution; and 2. Operate the RPS and storage infras-

tructure. The cost function takes into account individ-

uals satisfaction, energy availability, weather forecast

and storage levels. The feedback to the scheduling

layer is the output of the optimization module. The

algorithm is composed of two phases: 1. Optimiza-

tion with given and foreseen conditions; and 2. Search

of alternative scheduling scenarios whenever the de-

mand surpasses the local supply. The feedback is sent

back to the household individuals to accept the pro-

Wireless Power Transmission in Smart Cities: The wIshood - Wireless Smart Neighborhood

319

posed changes or proceed with the original scenario;

albeit requiring to buy electricity from elsewhere, e.g.

the national grid. The latter functionality of the OM is

to operate the RPS excess energy generation. This is

done mainly through management of the centralized

and distributed storage devices.

The weather forecast module objective is to pro-

vide support to the OM tasks. It is composed of two

parallel processes. Firstly, an artiﬁcial neural net-

work (ANN) algorithm performs fast, real-time and

on-demand estimations of short-term (i.e., hours to

a couple of days) weather conditions. Secondly, the

forecast layer is connected to a national weather fore-

cast system. This second process provides the nec-

essary information for decision-making of long-range

(i.e, weekly) estimates.

3.5 Control

The control layer principal tasks are: 1. Coordinate

the commands sent from the processing layer; and

2. Guarantee the safety operation of the RPS, electric-

ity distribution and storage infrastructure. The control

layer receives input from the processing and commu-

nication layers. The output is the dispatch of energy

from the RPS and central storage to the households

appliances, storage facilities, centralized storage and

into the national grid.

The backbone of the control layer are Adaptive

Robust Control Theory and Kalman ﬁltering. The de-

sign of the controller takes into account the uncertain

events occurring in the wIshood, e.g., trucks block-

ing wireless communication or infrastructure failure.

The metering devices constantly update the controller

of the electricity distributed over the wIshood. The

Kalman ﬁlter is a ﬁnal preprocessing phase of the me-

tering data before the control adapts to the changes of

the environment.

4 CHALLENGES

The wIshood ecosystem (see Fig. 3) poses a myriad

of challenges to be addressed by the research com-

munity. In the following we mention a few of the vast

possibilities and from different domains of expertise.

4.1 Wireless Power Transmission

Electricity transmission of domestic scale is by far the

most complex aspect to be addressed. Although suc-

cessful attempts of long distance WPT have been ac-

complished, they have been based on a free space en-

vironment (Ma et al., 2016). WPT attenuation is due

mainly to obstacles between source and destination

and atmospheric losses. Frequency spectrum can be

selected to minimize the latter although it should also

take into account interference with existing commu-

nication bands (Imura et al., 2016).

The design of transmitter and receiver coupling

systems is highly dependent of the material of the

core. Presently, the core is made of composite fer-

rite materials such as Mn-Zn and Ni-Zn. The former

is mostly employed because of its electric properties.

Nevertheless, a mayor concern of core manufacture

is scalability. Firstly, ferrite material are brittle and

prone to breakdown as size increases. Secondly, the

high permittivity of Mn-Zn leads to intense electric

ﬁelds in discontinuities. Thus, arching or discharge

occurs even in the presence of high dielectric mate-

rials. Lastly, frequency selection has a direct impact

upon the permittivity of the ferrite material and hence

the ability to guide the electric ﬂux (McLean and Sut-

ton, 2016).

4.2 Heterogeneous IoT

It is common to employ IoT motes from a variety of

vendors. Hence, the data gathered, from these de-

vices, cannot be used directly and must be converted

into a standard form. Moreover, employing IoT in

large sets can also result in spectrum scarcity. IoTs

often employ unlicensed spectrum. To account for

band saturation smart IoT should have cognitive ca-

pabilities i.e., dynamically switch between different

frequencies.

Efﬁcient bandwidth allocation techniques are of

paramount importance (Khan et al., 2016). Dense de-

ployment of IoT motes in a speciﬁc area can result

in severe bandwidth constraints. This is due to the

fact that IoT motes are, at a high-speed, continuously

transmitting data to a shared infrastructure and using

same unlicensed spectral bands. Bandwidth alloca-

tion, to the massive number of devices, poses strin-

gent constraints to current communication protocols.

4.3 Security

The lessons learned from recent IoT hacks makes

security of utmost importance. Coordinating secu-

rity mechanisms (e.g., software updates, malware de-

tection and identity management) present real con-

straints in highly federated environments. Security or-

chestration in the wIshood must incorporates threats

aware mechanisms from IoT but also from cloud com-

puting and SDN perspective.

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320

Scheduling

Control

Processing

Weather

Forecast

RPS / STORAGE

WPT

WSN

IoT Appliances

Smart Grid

Smart Home

Edge Cloud

Cloud Computing

Cloud Storage

Figure 3: Topology of the wIshood’s architecture. It is formed by the scheduling, information, communication, processing

and control layers. The RPS supplies energy to the households using WPT technology.

4.4 Energy-Harvesting-Aware Robust

Protocols

To efﬁciently exploit energy sources, robust com-

munication protocols are also vital. Although such

protocols have been thoroughly explored for conven-

tional energy-harvesting sensor networks, they can-

not be adopted for smart homes because of its unique

challenges. Exclusive communication protocols and

standards must be designed (Hou et al., 2016) for

commercialization of reliable and customizable im-

plementations.

4.5 Efﬁcient Power Management

Scavenged energy requires effective power manage-

ment among household appliances. We considered

two cases 1. Energy generated by RPS is less than

real-time demand; and 2. Power production is greater

than demand. Thus, to address these scenarios, efﬁ-

cient utilization and fair distribution of energy algo-

rithms need to be designed.

In the former, the energy should be fairly dis-

tributed among the households. A key aspect of

power orchestration is the time allocation for running

different appliances. For any excess power require-

ments the energy could be obtained from the smart

grid (see Fig. 3). In the second scenario, if the en-

ergy produced is more than required, the excess en-

ergy could be stored for later use or re-routed toward

other areas, e.g., sold to the smart grid. Thus, research

effort should go into coupling wake-up scheduling

schemes with harvesting schemes to ensure quality of

service requirements.

Apart from dispatch speciﬁc issues, energy trad-

ing with third parties must be taken into considera-

tion. This implies a Credits scheme for energy trading

between neighborhoods in different periods of time.

The dispatch algorithm should also take into account

the best time for selling electricity to the smart grid.

4.6 Appliances Management

A principal challenge of the processing layer is to

provide an optimized schedule of energy consump-

tion. The cost function of the optimization algorithm

will also take into consideration the users deﬁned pa-

rameters for the shiftable, throttleable or essential ap-

pliances. The unsupervised learning algorithm pro-

poses better scheduling based on previous data and

recent consumption trends. The complexities of the

non-linearities among IoT devices, household pref-

erences and uncertainties call for adaptable models,

faster learning and optimization algorithms.

5 CONCLUSIONS

This work presented a new framework of future smart

neighborhoods, the wIshood. The wIshood advances

the state-of-the-art of smart cities with an infrastruc-

Wireless Power Transmission in Smart Cities: The wIshood - Wireless Smart Neighborhood

321

ture for wireless transmission of electricity for res-

idential needs. Within the wIshood, the energy is

generated, stored and dispatched to the households.

We envision, an intensive deployment of IoT devices,

cloud computing and a wireless power transmission

of scale. This paper outlined the architectural founda-

tion and algorithms to address the challenges of such

an ecosystem.

Future work intends to simulate components of

the wIshood architecture. We plan to develop two al-

gorithms. Firstly, a reduction of household data to

stream to the Information layer. Secondly, a Recur-

rent Neural Network for energy demand forecasting.

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