Wireless Energy Recharge Protocols for Mobile Crowdsensing in

-3pt

.

......

Wireless Energy Recharge Protocols forMobile Crowdsensing in IoT Systems

Sotiris E. Nikoletseas

Computer Technology Institute and Press ”Diophantus” (CTI)and University of Patras, Greece

9th Summer School on IoT and Applications (SenZations 2014),Croatia, September 2014

Wireless Energy Recharge Protocols for Mobile Crowdsensing in IoT Systems 1 / 103

-3pt

.. Overview

A. Mobile Crowdsensing Systems (MCS)

B. Wireless Power Transfer Protocols

- Introduction, technology, models- Key Challenges

C. Three basic cases

- Single Mobile Charger (in homogeneous/heterogeneousnetworks)

- Multiple Mobile Chargers - Coordination- Hierarchical Multiple Mobile Chargers - Collaboration

D. Relevant Trends in Wireless Energy Research


-3pt

.. A. Mobile Crowdsensing Systems (MCS)

“Exploit the embedded sensory capabilities ofubiquitously present smart devices (likesmartphones and tablets) to provide a sensingplatform that is more sustainable (in terms ofmanagement and deployment costs), more agileand of higher precision than traditionalparadigms such as Wireless Sensor Networks”.

MCS represent a basic enabling technology forthe broader Internet of Things (IoT) vision.


-3pt

..A. Mobile Crowdsensing Systems (MCS)Key Design Issues for MCS

Key Design Issues:

Incentivization, motivating people to join

User acceptance

Trust/Privacy

Effectiveness/Efficiency/Longevity


-3pt

..A. Mobile Crowdsensing Systems (MCS)Incentives

Types of Incentives:

Flat

Ij =B

|A|(B: incentive budget, |A| : number of agents in region S)

Location/mobility aware

Ij =B

|σ||ASi |(|σ|: number of agents in a subregion |Si|)Quality-aware:

Ij =B

|σ||ASi |· qj∑

i qi

Behaviour-based: reward commitment and trustworthiness

Ij = Bfamej∑i famei


-3pt

..A. Mobile Crowdsensing Systems (MCS)Metrics and User Acceptance

Performance metrics:

Performance (e.g. Energy Saving)

Cost (incentive budget spent)

Social Welfare

SW =

∑A |luxAj − luxSAj

||A|

Sotiris E. Nikoletseas, Maria Rapti, Theofanis P. Raptis, Konstantinos Veroutis:

Decentralizing and Adding Portability to an IoT Test-Bed through

Smartphones. DCOSS 2014: 281-286


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..A. Mobile Crowdsensing Systems (MCS)The start: The HOBNET EU Project

Energy efficient buildings

Smart automation:

- sensors,- actuators (blinds, lights, doors, etc.)- control cubes (turns objects to

“smart”)

Characteristic scenaria:

- HVAC optimization- light control- CO2 monitoring

Constantinos Marios Angelopoulos, Sotiris E. Nikoletseas, Theofanis P. Raptis etal: A holistic IPv6 test-bed for smart, green buildings. ICC 2013.


-3pt

..A. Mobile Crowdsensing Systems (MCS)Major enhancement: The IoT-LAB EU Project

Internet of Things/Crowdsourcing

A crowdsourced energy use case:

- Users join the crowdsouring tool- They share localized ambient data- They express preferences- They automation tries to achieve a good trade-off betweenenergy saving and comfort


-3pt

..A. Mobile Crowdsensing Systems (MCS)Android Application

Discovers the availablebuilding devices

Uses different visualizationfor each device type

Show information about

the ambient sensors

- Temperature- Humidity- CO, CO2- Dust

Users can interact with the

building devices

- Sends CoAPmessages directly tothe Control Cubes


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..A. Mobile Crowdsensing Systems (MCS)Automated light scenario

Scenario runs as a service

- Uses input from heterogeneous devices- User enables or disables the scenario- User can choose the desired luminosity

level

Combines internal light readings and external

weather conditions

- Takes into account cloud percentage, it cancause reflections

Android smartphone is the coordinator

- Regulates the curtains on daylight- Turns on/off the lights inside the room

Reduction of energy consumption vs user

comfort

- User can choose a higher value for comfort- User can choose a lower value for reduction of

energy consumption


-3pt

..B. Wireless Power Transfer ProtocolsIntroduction

Recent advances in wireless energy transmission and batteriesmaterial:

...1 Wireless energy transmission:

Through strongly coupled magnetic resonances, 40%efficiency of transferring 60 watts of power over 2meters.Industry research demonstrated that it is possible toimprove transferring 60 watts of power over 1 meterwith efficiency of 75%.Wireless Power Consortium - Cooperation of Asian,European, and American companies in diverse industries.Working towards the global standardization of wirelesscharging technology.

...2 Batteries material:

Ultra-fast charging was recently realized in LiFePO4 bycreating a fast ion-conducting surface phase throughcontrolled off-stoichiometry.


-3pt

Commercial Prototypes (and even products) appear:

(a) an Acroname Garcia robot with a Powercast charger; (b) TelosB motes withPowercast receivers.

Prolonging Sensor Network Lifetime Through Wireless ChargingYang Peng, Zi Li, Wensheng Zhang, Daji Qiao

Iowa State University31st IEEE Real-Time Systems Symposium (RTSS 2010)


-3pt

..B. Wireless Power transfer Technology (I)Inductive Coupling

Works by magnetic field induction (i.e., an alternatingcurrent in a primary coil generates a varying magnetic fieldthat induces a voltage across the terminals of a secondarycoil at the receiver).

Example of inductive coupling: electrical transformer.

Applications: electrical toothbrush, charging pads for cellphones, laptops and tablets.

Strengths:

Simple, high power transfer efficiency in centimeterrange.

Weaknesses:

short charging distance, requires accurate alignment incharging direction.


-3pt

..B. Wireless Power transfer Technology (II)Electro-Magnetic Radiation

EM radiation emits energy from the transmit antenna of a powersource to the receive antenna via radiative EM waves.

Depending on the energy-emitting direction, it can be classified intoomni-directional radiation and unidirectional radiation.

Omni-directional EM radiation:

A transmitter broadcasts EMwaves in an assigned ISM bandand a receiver tunes to the samefrequency band to harvest radiopower.

Applications: charging a WSN forenvironmental monitoring(temperature, light, etc.)

Strengths:

Tiny receiver size

Weaknesses:

Rapid drop of power transferefficiency over distance, ultralower power reception

Unidirectional EM radiation:

When a clear line-of-sight (LOS)path exists, unidirectionalradiation can achieve high powertransmission by using amicrowave or laser beam.

Applications: SHARP unmannedplane

Strengths:

Effective power transmission overlong distance (kilometer range)

Weaknesses:

Requires LOS and complicatedtracking mechanisms, inherentlylarge scale of devices.


-3pt

..B. Wireless Power transfer Technology (III)Magnetic Resonant Coupling

Is based on the well-known principle of resonantcoupling (i.e., by having magnetic resonant coils operateat the same resonance frequency so that they are stronglycoupled via nonradiative magnetic resonance induction).

Applications: charging mobile devices, electrical vehicles,implantable devices and WSNs.

Strengths:

High efficiency over several meters under omni-direction,not requiring LOS and insensitive to weather conditions

Weaknesses:

High efficiency only within several meter range.


-3pt


These advances offer new possibilities for energy management.

A new network paradigm..

......

A WRSN consists of

a set of sensor motes: that are deployed over an area ofinterest; the network area

a Sink: a special node of the network towards whichsensory data is routed

a Mobile Charger: a mobile entity, with significantenergy reserves, that can recharge the sensor motes viawireless energy transfer


-3pt


Wireless Rechargeable Sensor Networks (WRSNs)enable:

the highly constrained resource of energy to be managedin great detail and more efficiently/timely

the energy management to be performed passively bysensors, with no computational and communicationoverhead

the energy management to be studied and designedindependently of the underlying routing protocol


-3pt


Remarks

The wireless recharge problem in WSNs may look similarto other problems (e.g. data collection via mobile sinks)

However, it admits special features and new trade-offsthat necessitate a direct approach

For instance, what is the amount of energy each moteshould receive?

Note that charger optimisation problems are (inherently)computationally hard (see NP-completeness of the ChargerDispatch Decision Problem -CDDP in [1])

[1] Constantinos Marios Angelopoulos, Sotiris E. Nikoletseas, Theofanis P.

Raptis, Christoforos Raptopoulos, Filippos Vasilakis: Efficient energy

management in wireless rechargeable sensor networks. MSWiM 2012:

309-316


-3pt

..B. Wireless Power Transfer ProtocolsKey Design Aspects

Key design aspects

What should be the split of the total available energybetween the charger and the sensor motes?

Given that the energy reserves of MC is finite, to whatextent should each sensor be charged?

Which are good trajectories for the MC to follow inorder to charge the sensor motes?


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..B. Wireless Power Transfer ProtocolsOur Methodological Approach (I)

Our aim

to improve the energy efficiency of the network; toprolong the network lifetime

to also improve key network properties (e.g. quality ofnetwork coverage and the robustness of data propagation)

to not couple the charging process with the datapropagation scheme

to design distributed, efficient and adaptive rechargingschemes, that are agnostic of the routing protocol

In fact, we aim at schemes that implicitly adapt to anyrouting protocol and to network diversities;

e.g. heterogeneous placements


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..B. Wireless Power Transfer ProtocolsOur Methodological Approach (II)

Most state of the art solutions require a (limited) globalknowledge of the network.

On the contrary, the solutions proposed in this line ofresearch are fully distributed and adaptive, and relymainly on local information.

Our strategy for the Mobile Charger can be used incombination with any underlying routing protocoland adapts to the dynamic energy evolution in thenetwork.


-3pt

.. C. Three basic cases

1. Single Mobile Charger. Two sets of protocols:

a. uniform networks - energy awarenessb. heterogeneous networks - energy and traffic awareness

2. Multiple Mobile Chargers - Coordination

3. Hierarchical Multiple Mobile Chargers - Collaboration


-3pt

..C1a. The ModelDeployment, energy model and charging model

Deployment:

Three types of devices: static sensors, Mobile Charger,static Sink.

N sensors uniformly distributed at random in circular areaof radius R.

The Sink S lies at the center of the circular area.

All sensors have the same data generation rate.

Energy model:

Etotal = Esensors + Einitcharger.

Emaxsensor =

EsensorsN .

Transmission cost: dα, 2 ≤ α ≤ 6 (here α = 2).

Charging model:

Only one sensor may be charged at a time from the MobileCharger.

We assume the charging time is equal at every sensor.Wireless Energy Recharge Protocols for Mobile Crowdsensing in IoT Systems 23 / 103

-3pt

.. C1a. The Charger Dispatch Decision Problem

Given:1. a set S of sensors each one capable to store E units of energy,

2. for each sensor s ∈ S a list Ls of pairs (tjs, ejs), j ≥ 1 in which tjs

corresponds to the time that the j-th message of s was generated and ejsthe energy that the sensor used to transmit it,

3. an |S| × |S| matrix D, where Di,j is the distance between sensors i andj,

4. a mobile charger M which can charge a sensor in one time unit to itsinitial energy.

The Charger Dispatch Decision Problem (CDDP) is to determinewhether there is a feasible schedule for M to visit the sensors so thatno message is lost due to insufficient energy.

.Theorem..

......CDDP is NP-complete. (proof: reduction from Geometric-TSP problem)


-3pt

.. C1a. Trade-Offs of the Charging Process

Trade-offs we identify and try to optimize:

- Energy percentage available to charger:

How much energy should the Mobile Charger be initiallyequipped with?More energy to the Mobile Charger leads to better onlinemanagement of energy in the network.However, it also means that the sensor motes will beequipped with a lower initial energy(Etotal = Esensors + Einit

charger).

- Full versus partial charging:

Full charging of a mote - maximization of the time intervalof revisiting that mote.However, the Mobile Charger will have increasingly lessenergy to distribute to the rest motes.

Partial charging! ≈ EcurrMC

EinitMC

Emaxsensor


-3pt

..C1a. Traversal Strategies of the Mobile ChargerSimple traversal strategies

Global knowledge strategy: visits the sensor with the minimumproduct of energy times its distance from the position of the charger.

∼ min{(

1 + EcurrEinit

)·(1 + distcurr

2R

)}Random walk strategy: pi,j = 1

deg(i),

pi,j : probability that the charger will visit neighbor node j after nodei,deg(i): the number of neighbouring nodes of node i.

Spiral strategy:

Covers the whole network in asequential way and avoids fre-quent overlaps.

Diameter strategy:

α1

α2

Quickly moves between diversesubregions.


-3pt

.. C1a. Our adaptive circular traversal strategy

while EcurrMC > 0 do

Etmp = 0for every i ∈ S do

Etmp+ = eci

Charge until ei ≈Ecurr

MC

EinitMC

· Emaxsensor

end forEcurrent =

Etmp

|S| =∑

ei|S|

if Ecurrent ≈ Einit thenif Eprevious ≥ Ecurrent then

Keep directionelse

Change directionend if

end ifend while

The radius of the trajectory adaptsto the energy depletion rates of eachsubregion.

ei: residual energy of node i.Ecurr

MC : current energy of the Mo-bile Charger.S: the set of sensors to be chargedat a given ring.Eprevious: Ecurrent of the previ-ous ring.


-3pt

..C1a. Performance EvaluationExperimental settings

Simulation environment: Matlab 7.11.

Sensors deployed uniformly at random in a circular area.

r =√

c lnNπN , c > 1 - random instance connected with high

probability.

Underlying routing protocols:...1 Greedy hop by hop protocol...2 Clustering protocol...3 Energy balance protocol (includes long transmissions)

Performance metrics:...1 alive nodes over time...2 node energy distribution (energy map)...3 average node degree over time...4 coverage ageing

We investigate step by step each one of theaforementioned trade-offs.


-3pt

.. C1a. Full versus partial charging

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full chargingpartial charging

Full vs partial charging for the energy balance protocol.

Partial charging achieves longer lifetime.

Using full charging, the Mobile Charger’s energy isdepleted earlier.


-3pt

.. C1a. Energy percentage available to the charger

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Percentage of the initial network energy given to the Mobile Charger

Aliv

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afte

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00 e

vent

s

Percentages of the initial total energy available to the charger for the energybalance protocol (partial charging selected).

The impact of the energy given to the charger is notmonotone. More energy does not equal to better performance.

The lifetime is maximized when the Mobile Charger is equippedwith a moderate fraction of total initial energy.


-3pt

.. C1a. Traversals comparison

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900

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Events

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our chargerspiral chargerrandom chargerdiameter chargerglobal knowledgecharger

Mobile Charger traversal strategies for the energy balance protocol (partialcharging selected, 20% of the initial energy given to the Mobile Charger).

Our adaptive traversal strategy approaches the globalknowledge traversal behaviour.


-3pt

.. C1a. Traversals comparison

greedy energy balance clusteringglobal knowledge 48974 64330 57458

spiral 64167 64167 64167random walk 181135 222462 222447

diameter 152252 172734 173169our adaptive traversal 42412 37856 38641

Table: Distance travelled by chargers

For all underlying routing protocols used, the MobileCharger using the adaptive traversal travels muchshorter distance.


-3pt

.. C1a. Overall improvements on the routing protocols

Selected Mobile Charger configuration:

Partial charging.

20% of the total available initial energy.

Our proposed adaptive traversal strategy.


-3pt

.. C1a. Alive nodes over time

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without chargerwith charger

Alive nodes over time for the energy balance protocol.


-3pt

.. C1a. Energy map - Energy Balance Protocol

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Energy map for the energy balance protocol (left: without charger, right: withcharger).

The darkest the colour, the less is the nodal residual energy.

In energy balancing routing, distant nodes tend to be stressed more.

Although the overall network energy is lower, energy balancing isachieved.


-3pt

.. C1a. Energy map - Greedy Multi-hop Protocol

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Energy map for the greedy multi-hop protocol (left: without charger, right: withcharger).

In greedy routing, nodes closer to the Sink tend to bestressed more.

Using the Mobile Charger, energy balancing isachieved.


-3pt

.. C1a. Energy map- Clustering Protocol

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Energy map for the clustering protocol (left: without charger, right: with charger).

In clustering routing, distant nodes tend to be stressedmore.

Using the Mobile Charger, energy balancing isachieved.


-3pt

.. C1a. Average node degree over time

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Average node degree over time for the energy balance protocol.

Significant improvement.


-3pt

.. C1a. Coverage ageing

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< 2 covered2 covered3 covered> 3 covered

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Coverage ageing for the energy balance protocol (left: without charger, right: withcharger).

Each colour represents a k-coverage of the network (k = 1, 2, 3, ...).

Without the use of a charger, coverage level is decreased over time.

Mobile Charger maintains network coverage.


-3pt

.. C1a. Conclusions

- Best strategies:

Partial recharging of the motes.Mobile Charger with a moderate fraction of the totalavailable energy.Circular trajectory around the Sink of a radius whichlocally adapts to spatial variations of energy in the network.

- Significant performance gains with respect to variousmetrics.


-3pt

.. C1b. Heterogeneity Awareness

Contribution

criticality of a node: a new network attribute capturingboth energy consumption and traffic flow

based on criticality we suggest a particular amount ofenergy each sensor node should be charged with

we design three mobility strategies for the MC, eachone with different levels of network knowledge;

global knowledgelimited knowledgereactive knowledge


-3pt

.. C1b. The Network Model

The network model (same as before)

Static sensor motes and Sink. One MobileCharger (MC)

Circular network area tessellated into sectorsby slices and rings

Each mote is aware of its location and belongsto one sector

Each mote has a constant size memory and isassigned a data generation rate λi ∼ U [c, d]

⇒Establishing Heterogeneity in the network

...1 Deployment of few sensors u.a.r. over the network area toestablish connectivity (Random Geometric Graphs threshold)

...2 Each sector Sij is assigned a relative density δij ∼ U [a, b]

...3 In sector Sij are deployed nij =n∑

i′,j′A

i′j′Aij

δi′j′δij

sensors

n: total number of sensors, Aij : the area of sector Sij


-3pt

.. C1b. The Energy Model

The energy model (same as before)

Total available energy in the networkEtotal = Esensors + EMC(tinit)

Point-to-point charging; charging occurs to one mote at thetime

Charging time proportional to the amount of energy delivered tothe mote

Energy consumption during wireless transmissions isproportional to the message size and the square of thetransmission distance

We consider both single-hop/cheap and long-distance/energyconsuming transmissions


-3pt

.. C1b. WRSN Networking Aspects

Node Criticality: a new network attribute.

Node Criticality captures the “importance” of each nodeinside the network in terms of:

traffic served by aparticular node

energy consumed by aparticular node

Traffic served captures differentnetworking aspects than the en-ergy consumption rate

The purpose of this attribute is to indirectly prioritize thevarious nodes


-3pt


Criticality of node vi at time t:

ci(t) = fi(t) · ρi(t)

where:

fi(t) = 1− generation rate of node vitraffic rate of vi since tMC

= 1− λi

λi +mi(t)t−tMC

is the normalized traffic flow served by node vi, and

ρi(t) =energy consumed since last charging

max node energy since tMC

=Ei(tMC)− Ei(t)

Ei(tMC)= 1− Ei(t)

Ei(tMC)

is the normalized energy consumption by time t, since the last

charging. It is 0 ≤ fi(t), ρi(t), ci(t) ≤ 1.


-3pt


Charging Extent

Over time the energy inside the network diminishes → theenergy of MC is more valuable

Straightforward charging policies includea) delivering to each node the same amount of energyb) delivering to node vi energy depending on its residual energy

However these policies neglect the energy evolution of thenetwork (nodes differ in location, generation rate, role in thenetwork, etc)

We let the MC recharge each node proportionally to its energyconsumption rate, criticality and to the reserves of MC

Ei(t+ tc) = Ei(t) + ci(t) · eMC(t) ·∆ei(t)

ci(t)·eMC(t)·∆ei(t) =

(1− λi

λi +mi(t)t−tMC

)· EMC(t)

EMC(tinit)· (Ei(tMC)− Ei(t))

2

Ei(tMC)


-3pt

.. C1b. Global Knowledge Protocol - GKP

On-line, centralized method that uses criticality as a rankingfunction

The MC prioritizes node recharges based on

mini

{(2− ci(t)) ·

(1 +

disti2D

)}where disti: the distance of sensor i from the MC andD: the network radius

In other words, GKP prioritizes nodes with high criticalityand small distance to the MC

Requires global knowledge of the state of the network (greatoverhead, bad scaling)

However, it represents an upper bound on performance


-3pt

.. C1b. Limited Reporting Protocol - LRP

Efficiently and distributively “simulates” the GKP by following alimited reporting strategy

1 In each Sector few motes are elected as reporters(binomially distributed)

2 Reporters propagate data on their criticality to the Sink

3 The Sink informs the MC, which then adapts its trajectory

Accuracy vs communication overhead trade-off

The total number of reporters:

κtotal = hD

rlog n

where h = 1− ab a network density heterogeneity parameter;

if a ≫ b, then more reporters in the network . D: networkdiameter, r: mote range.


-3pt

.. C1b. Reactive Trajectory Protocol - RTP

Efficient, adaptive solution for dynamic networks(e.g. varying event generation rates per Sector)

When the residual energy of node vi falls below a threshold,vi propagates an alert message to its neighbourhood

Via limited flooding an alert tree structure is createdrooted at vi

At each level of the tree the alert intensity decreases ⇒the depth of the tree depends on the initial alert level

Several alert levels are defined and different trees can beaggregated

The MC patrols the network and once a tree is discovered, itfollows the “stream” of alerts towards the root


-3pt

.. C1b. Other Known Protocols

Local Knowledge Protocol - LKP (presented previously)

The MC follows a circular trajectoryaround the Sink

The radius of the trajectory varies andadapts to the energy depletion rates ofeach subregion

GreedyPlus (by another group)

Takes into account both network’s lifetime and traveling distanceby MC to create a charging sequence

All sensor nodes estimate their remaining lifetime and anaggregated report is propagated to the Sink

The Sink computes (off-line) and propagates to the MC theoptimal schedule

Very strong knowledge assumption (MC is aware of all IDsand locations)- very high communication overhead


-3pt

.. C1b. Experimental Set-up

Experiments conducted in Matlab 7.11

For each experiment a large number of data propagations isgenerated and the average value is taken

Each experiment is repeated 100 times for statisticalsmoothness

Statistical analysis of the findings (median, lower andupper quartiles, outliers of the samples) demonstrate veryhigh concentration around the mean

Since our MC trajectory heuristics are agnostic to theunderlying routing protocol, we conduct three full sets ofexperiments assuming multi-hop propagation,clustering and energy balancing routing protocols


-3pt

.. C1b. Node Deployment

Nodes are deployed over a circular, planar network area

We assume that two nodes u, v communicate iff ∥u, v∥ ≤ r

First, a small portion of the nodes is deployed u.a.r. inorder to establish network connectivity

This portion is defined by the communication threshold of

Random Geometric Graphs rc =√

lnnπn

The rest of the nodes are deployed in Slices and Sectors toachieve the desired heterogeneity as described earlier


-3pt

.. C1b. Investigating the Protocol Parameters

1) Node Criticality: captures node’s diversity in terms of energydissipation and flow rate

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node energy criterionnode criticality criterion

Alive nodes over time and average routing robustness(MC uses LKP - energy balance routing protocol)

Clearly criticality is a better criterion than simple residualenergy as it greatly extends network lifetime while maintaining

a stronger degree of connectivity


-3pt

.. C1b. Investigating the Protocol Parameters

2) Number of Reporters for LRP

1% 3% 5% 10% 20% 30%260

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Reporters ratio

Num

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Reporters ratio

Perc

enta

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erhe

ad

Alive nodes and communication overhead for various κtotal values in LRP, after6000 generated events

Trade-off:

High numbers of reporters provide more detailed information onthe state of the network

However, they also infer communication overheadoptimal set-up: 5% reporters → 10% overhead


-3pt

.. C1b. Performance Evaluation

Number of Alive nodesThe number of nodes with sufficient residual energy to operatethroughout the experiment

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no chargerLRPRTPLKPGreedyPlusGKP

Number of Alive nodes over time

Significant improvements in network lifetime

LRP and RTP approach the performance of the powerfulGKP (global knowledge)

Also, they outperform LKP and GreedyPlus


-3pt


Criticality Map

Graphical representation of the spatial distribution of energydissipation combined with data flow information after 4000events

No charger LKP LRP

RTP GreedyPlus GKP

GKP achieves a balanceddistribution of criticality(global knowledge)

Our proposed LRP and RTPachieve similar performance

LKP perfomance depends onthe routing protocol


-3pt


Routing Robustness

The number of alive neighbours (node degree) over time:

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no chargerLRPRTPLKPGreedyPlusGKP

Average routing robustness

Captures the level ofnetwork connectivity

Again, LRP and RTPoutperform other protocolsby approaching the GKP


-3pt


Number of strongly connected graph components

An overall measure of connectivity quality in a sensor network

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Connected graph components over time

Disconnected componentscannot communicate witheach other and the Sink

A small number of connectedcomponents improves datadelivery latency

LRP maintains a singlestrongly connectedcomponent


-3pt


Coverage AgeingEvolution of coverage of 1000 points selected u.a.r. inside thenetwork area

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(d) RTP (e) GreedyPlus (f) GKP


-3pt

.. C2. Multiple Mobile Chargers - Coordination

Our methods distinguish the network operations in threeseparate levels: the coordination procedure, thecharging process and the routing mechanism.

We identify and try to optimize the followingtrade-offs:

...1 assuming a certain number of Mobile Chargers in thenetwork, in what way should they coordinate.

...2 given that the Mobile Chargers have coordinated, whatare good trajectories for them to follow.

We provide four new coordination and charging protocolsbased on their network knowledge (global/local/noknowledge) and their processing ability(distributed/centralized).


-3pt

..C2. The ModelDeployment

Deployment:

Three types of devices: stationary sensors, Mobile Chargers,stationary Sink.

N sensors distributed uniformly at random in circular area of radiusR.

The Sink S lies at the center of the circular area.

The sensor nodes propagate data to a Sink using a routing protocol.

Heterogeneous data generation rate assumption.

Each Mobile Charger is assigned to a region (e.g. for K = 8chargers):

φj−1

φj

φj+1


-3pt

..C2. The ModelEnergy and charging model

Energy model:

Etotal = Esensors + EMCs(tinit).

Emaxsensor =

EsensorsN .

EmaxMC = EMCs(tinit)

K .

Transmission cost: dα, 2 ≤ α ≤ 6 (here α = 2).

Charging model (similar to before):

Only one sensor may be charged at a time from aMobile Charger.

We assume that the charging time is equal at everysensor.

We assume maximal charging efficiency.


-3pt

..C2. The ModelThe demarcated protocol phases

Coordination phase.Energy dissipation rate among Mobile Chargers maynot be the same.They periodically communicate with each other and dealout their charging regions fairly (a weaker MobileCharger in terms of energy should be assigned to a smallernetwork region).

In the centralized case the coordination is performedusing information from all K chargers.In the distributed case, status of only its neighbouringchargers is known - more secluded coordination betweenclose chargers.

Charging phase.We give emphasis to the amount of knowledge possessedby protocols in terms of locality.We distinguish the protocol’s knowledge amount amongglobal knowledge, local knowledge, no knowledgeand reactive knowledge.


-3pt

..C2. The protocolsCentralized Coordination protocol CC

Coordination phase (centralized coordination).In order to compute the size of the region of charger j, itsuffices to compute the central angle ϕj .Each charger is assigned to a region of size analogousto its energy level, so that the energy dissipation amongthe chargers is balanced.

ϕj = 2π · Ej∑Kj=1 Ej

, whereK∑j=1

ϕj = 2π.

Charging phase (no knowledge on the network).“Blind” scanning of the assigned region.The Mobile Charger starts form the Sink and traverses anexhaustive path until it reaches the boundaries of thenetwork area.Advantage: the Mobile Charger covers the whole Slice.Disadvantage: movement is not adaptive.


-3pt

..C2. The protocolsDistributed Coordination protocol DC

Coordination phase (distributed coordination).

The chargers distributively define their Slice limits (the two radiithat define the Slice), according to the size of the region they canhandle.Each charger can shift their right and left Slice limits resulting ineither a widening or a shrinkage of the region they cover.

ϕ′j = ϕj +∆ϕl

j +∆ϕrj

Two critical charger parameters are used, the current energy levelEj and the energy consumption rate ρj .Computation of the left limit alteration ∆ϕl

j :

if min{Ej , Ej−1} = Ej then

∆ϕlj = −ϕj · |ρj−1−ρj |

max{ρj−1,ρj}else

∆ϕlj = ϕj−1 · |ρj−1−ρj |

max{ρj−1,ρj}end if

Similarly for the right limit.

Charging phase (no knowledge on the network).

Same as Centralized Coordination protocol CC (“Blind” scanning).


-3pt

..C2. The protocolsCentralized Coordination Global Knowledge protocol CCGK

Coordination phase (centralized coordination)We integrate the global knowledge assumption in thecoordination phase.The region of interest of charger j is a cluster of nodes,Cj . Node i selects a charger j′ that is close and with highenergy supplies

j′ = argminj

{(1 +

distij2R

)·(2− Ej

EmaxMC

)}Charging phase (global knowledge on the network)

Energy and distance as a ranking function.Charger j charges a sensor i′ that is close and with lowresidual energy.

i′ = argmini∈Cj

{(1 +

distij2R

)·(1 +

Ei

Emaxsensor

)}CCGK is a centralized global knowledge upperbound of performance.


-3pt

..C2. The protocolsDistributed Coordination Local Knowledge protocol DCLK (I)

Coordination phase (distributed coordination)- Same as Distributed Coordination protocol DC(ϕ′

j = ϕj +∆ϕlj +∆ϕr

j).

Charging phase (local knowledge on the network)- Divide each Slice into Sectors

- Eminjk is the lowest nodal residual energy level in the

Sector Sjk.- Emin+∆

jk is an energy level close to Eminjk :

Emin+∆jk = Emin

jk + δ · Emaxsensor

Eminjk

, δ ∈ (0, 1).


-3pt

..C2. The protocolsDistributed Coordination Local Knowledge protocol DCLK (II)

Charging phase (continued)

- N(Sjk) is the number of nodes in Sector Sjk with

residual energy between Eminjk and Ejk

min+∆:

N(Sjk) =

Emin+∆jk∑

e=Eminjk

N(e)

where N(e) is the number of nodes with energy level e.- Charger j charges Sector Sjk which maximizes theproduct

maxSjk

{N(Sjk) · (Emaxsensor − Emin

jk )}.

- Intuition: Select a Sector containing a large number ofsensor nodes that require more energy than othernodes throughout the network.


-3pt

..C2. The protocolsCentralized Coordination Reactive Knowledge CCRK

We use a state of the art protocol for comparison purposes1 (CCRK).

Coordination phase (centralized coordination)

- Similar to that for shared memory access in operating systems.- The Sink maintains a 0− 1 valued node list. Once a sensor is

chosen, its value is set to 1 (locked). Otherwise, it is 0.

Charging phase (reactive acquisition of knowledge)

Message propagation overhead in order to acquire global

knowledge.

- Weighted sum of traveling time from node i to i′ and the residuallifetime of i′,

wii′ = αtii′ + (1− α)Li′ .

- α = 1: the algorithm reduces to nearest node selection.- α = 0: the algorithm picks the node with the earliest battery

deadline.

1C. Wang, J. Li, F. Ye, and Y. Yang. “Multi-vehicle coordinationfor wireless energy replenishment in sensor networks”, IPDPS, 2013


-3pt

..C2. The protocolsProtocol details

Protocol Coordination Charging

CC Centralized No knowledge

DC Distributed No knowledge

CCGK Centralized Global knowledge

DCLK Distributed Local knowledge

CCRK Centralized Reactive knowledge

Table: Protocol phase details.


-3pt

..C2. Performance EvaluationExperimental settings

Simulation environment: Matlab 7.11.

Sensor nodes deployed uniformly at random in acircular area.

Each sensor node chooses independently a relative datageneration rate λi ∈ [a, b] (where a, b constant values)according to the uniform distribution U [a, b].

r =√

c lnNπN , c > 1 - random graph instance connected

with high probability.

Performance metrics:...1 alive nodes over time...2 connected components over time...3 routing robustness...4 coverage ageing...5 traveling distance


-3pt

..C2. Performance EvaluationProtocols’ impact on network properties

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no chargerCCRKDCLKDCCCCCGK

Alive nodes over time.

Significant improvements in network lifetime.

DC approaches the performance of CC

DCLK approaches the performance of CCGK and outperforms CCRK.


-3pt


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no chargerCCRKDCLKDCCCCCGK

Average routing robustness.

Number of alive neighbouring nodes over time for each node.

Captures the level of network connectivity.

Follows the same pattern as the lifetime metric.


-3pt


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alive neighbours < 76 < alive neighbours < 1312 < alive neighbours < 1516 < alive neighbours

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(d) DCLK (e) CC (f) CCGK

Detailed routing robustness.


-3pt


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CCRKDCLKCCGKDCno chargerCC

Graph connected components over time.

- Overall measure of connectivity quality in a sensor network.

Disconnected components cannot communicate with each other.

DCLK and CCRK maintain small numbers of connected components.


-3pt


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(d) DCLK (e) CC (f) CCGK

Coverage ageing.


-3pt

..C2. Performance EvaluationTraveling distance

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CCRKCCDCCCGKDCLK

Distance traveled by Mobile Chargers.

Reflects the efficiency of the coordination procedure/charging process.

Leads to useful conclusions about the balance of the charger activity.

- CCGK, CCRK and DCLK travel less distance than DC and CC.


-3pt

..C2. Performance EvaluationTraveling distance

19%

19%

17%

19%

15%

13%16%

9%

21%

7%

26%

21%

13%

19%

17%20%

17%

15%

(a) CCGK (b) DC (c) CCRK

15%

22%

17%

19%

15%

13%18%

22%

18%

22%

8%

10%

(d) DCLK (e) CC

Distance traveled per Mobile Charger.Wireless Energy Recharge Protocols for Mobile Crowdsensing in IoT Systems 78 / 103

-3pt

.. C2. Conclusions

- Significant performance gains with respect to variousmetrics.

- Impact of knowledge.

CC, DC outperformed by improved alternatives CCGK,DCLK.DCLK protocol outperforms CC protocol.

The coordination procedure is less significant than thedesign of the traversal (when comparing differentamounts of knowledge).

CCRK is provided with global knowledge on the networkbut introduces message overhead.

The degradation from global knowledge to localknowledge assumption (DCLK) is more efficient than acostly acquisition of global knowledge.


-3pt

.. C3. Other Design Approaches

Simultaneous Wireless Charging

Collaborative Wireless Charging

Hierarchical Collaborative Wireless Charging

Wireless Charging in Mobile Sensor Networks


-3pt

..C3. Other Design ApproachesSimultaneous Wireless Charging

It allows energy to be transferred to multiple receivingdevices simultaneously.

The efficiency of charging multiple devices is higher thanthat of charging each device individually.

The key advantage of this approach is that it reduces thenumber of power sources (i.e., the Mobile Chargers) thatare needed.

Representative paper:Liguang Xie, Yi Shi, Y.Thomas Hou, Wenjing Lou, Hanif D. Sherali and ScottF. Midkiff, “On renewable sensor networks with wireless energy transfer:The multi-node case”, SECON 2012


-3pt

..C3. Other Design ApproachesCollaborative Wireless Charging

The Mobile Chargers are not only equipped with antenna to send energywirelessly and charge devices (e.g. sensor nodes) but also are equipped withantennas to receive energy wirelessly in order to get themselvesrecharged.

This capability allows the Mobile Chargers to charge each other.

A balanced energy consumption between the Mobile Chargers. ⇒balanced energy consumption between sensor nodes. ⇒ prolong thenetwork lifetime.

Representative paper:Sheng Zhang, Jie Wu and Sanglu Lu, “Collaborative mobile charging forsensor networks”, MASS 2012

MC MC MC

sensor

nodes

MC MC MC


-3pt

..C3. Other Design ApproachesHierarchical Collaborative Wireless Charging

We propose a classification of Mobile Chargers in the following two groups:the hierarchically low Mobile Chargers (called just MobileChargers) which are equipped with antennas to both send and receiveenergy but are responsible of charging only sensor nodes (notother Mobile Chargers) andthe hierarchically high Mobile Chargers (called Special Chargers)which are equipped only with antennas to send energy and areresponsible of charging only Mobile Chargers. Also, SpecialChargers have larger battery capacity than Mobile Chargers.

Representative paper:Adelina Madhja, Sotiris Nikoletseas and Theofanis P. Raptis,“Hierarchical,collaborative wireless charging in sensor networks”, Technical report, 2014

SC

MC MC MC

sensor

nodes

SC

MC MC MC


-3pt

..C3. Other Design ApproachesWireless Charging in Mobile Sensor Networks

In such networks not only Mobile Chargers are mobile butalso the sensor nodes are mobile.

The mobility of sensor nodes introduces new aspects of thecharging procedure and may allow the use of a lowernumber of Mobile Chargers to achieve specific performancegains.

The Mobile Chargers have to take into consideration themobility level of sensor nodes in order to perform thecharging procedure efficiently.

Representative paper:Haipeng Dai, Lijie Xu, Xiaobing Wu, Chao Dong and Guihai Chen, “Impact ofmobility on energy provisioning in wireless rechargeable sensor networks”,WCNC 2013


-3pt

.. D. Relevant Trends in Wireless Energy Research

1. Radiation Awareness

2. Energy Flow

3. Ambient Energy Harvesting


-3pt

.. D1. Radiation Aware Wireless Recharge

Note: The electromagnetic fields (EMF) created bychargers can be quite strong.

Challenge: How to schedule charging to avoid overlapsand cumulation of high EM radiation.


-3pt

.. D1. Radiation Aware Wireless Networking

Imagine a person moving in a smart building withabundant heterogeneous wireless networking (WiFi,Blue-tooth, ZigBee, Cellular etc),

carrying wearable, on-body or even implantedwireless devices (such as smart phones, medicalequipment and tiny smart sensors).

The additive/correlated impact of electromagneticradiation to the human and any carried nano-scale devicesand vital equipment can be dangerous and is worthstudying and controlling.


-3pt

.. D1. Motivation

Investigate the aspect of electromagnetic radiation inmodern and future heterogeneous wireless networks, from adistributed networking perspective.

Almost all wireless devices operate in frequencies of thenon-ionizing spectrum (100 kHz300 GHz)

The impact of non-ionizing frequencies on humans: thermaland nonthermal effects.

Many scientists worry for non-thermal effects (far belowestablished safety levels) which have not been investigatedmuch.


-3pt

.. D1. Basic definitions and preliminaries (I)

Radiation at a point: the sum of radiation created by allnodes close to the point

R(x⃗) =∑u∈V

r2

(1 + dist(x⃗, u))2

Radiation along a path: radiation that an entity movingalong path P receives

R(P ) ≈m−1∑i=0

R(x⃗i) · d

where r: wireless transmission range and

d =|P |m

for large m


-3pt

.. D1. Basic definitions and preliminaries (II)

Point radiation in random geometric graphs

Random geometric graph: The graph Gn,r = (V,E) withvertex set the set of n points (sensors deployed uniformly atrandom in a target area A) and edge set all pairs of verticeshaving euclidean distance at most ri.e. E = {(u, v) ∈ V 2 : dist(u, v) ≤ r}

E[R(x⃗)] ≈ 2nπr2

|A|

(log(1 + r)− r

1 + r

)

V ar[R(x⃗)] ≈ 2nπr4

|A|r3 + 3r2

6(1 + r)3−4nπ2r4

|A|2

(log(1 + r)− r

1 + r

)2


-3pt

.. D1a. The Minimum Radiation Path Problem

Given a Sensor Network G deployed in a target area A, astarting point A and a target point B: find a path P in thetarget area that starts from A, ends at B andminimizes R(P).

We provide:

The (offline) optimum path given by a linear program.

Three online approaches:

Minimizing the total distance - Algorithm MinDMinimizing the next step radiation - Algorithm MinR

Weakness: the total distance traveled is not taken intoaccount ⇒ the resulting path can be quite long.

A dichotomy algorithm - Algorithm MinDRD

Optimizes the radiation/progress-to-destination trade-off.


-3pt

.. D1a. Performance Evaluation

Grid

Mobility

Gn,r

The linear program gives the minimumsolution in terms of radiation.

MinDRD is near optimal.

MinRs performance is the worst: thealgorithm tries to minimize the next stepradiation and this can cause small progreestowards the target.

Because of mobility the naive MinDheuristic outperforms MinDRD


-3pt

.. D1b. Radiation aware data routing

We focus on the canonical problem of data routing inWireless Sensor Networks

We try to optimize the low radiation vs latency trade-off

We present greedy and oblivious heuristics in terms ofradiation

Then, we present two heuristics that are

Radiation awareOn-line andDistributed

We combine them with three temporal spreading schemesthat use local network properties:

local node densitydistance to the data destination

Performance Evaluation


-3pt

.. D1b. Network Model

n sensors deployed in a 2D area A ⊂ R2.

For two points x⃗, y⃗ ∈ A, we denote by dist(x⃗, y⃗) theEuclidean distance between them.

r: wireless transmission range.

λ: Poisson process modelling the data generation rate.

Te: an exponential random variable with parameter λ′

representing the duration of a transmission, where λ′

depends on the data packet size and the environment.

t(e) the time of occurrence of event e

B the base station, i.e. data destination

Sensor motes are aware of their exact position


-3pt

.. D1b. Formal Definition

We define the radiation at point x⃗ ∈ A caused by sensor vbecause of Te data transmissions as

Rx⃗,e,v = Br2

(1 + dist(x⃗,v))2Te, (1)

where B is a constant depending on the environment

radiation is captured mainly in terms of power density

also, the impact of wireless transmission’s duration is linear


-3pt

.. D1b. Performance metrics

Total radiation during a time interval: At point x ∈ Afrom data transmissions due to events occurring in [t1, t2]

Rx([t1, t2]) =∑v

∑e:t1≤time(e)≤t2

Rx,e,v. (2)

Maximum radiation in a time interval: Given a small timedistance τ > 0, the maximum radiation at x within [t1, t2] is therandom variable

MaxRx([t1, t2], τ) = maxt1≤t≤t2−τ

Rx([t, t+ τ ]). (3)

Latency: the time needed for all generated messages to bedelivered to the sink.


-3pt

..D1c. Relevant TrendsRadiation - Safe Charging Problem (SCP)

The Safe Charging Problem (SCP) is defined as howto schedule power chargers (i.e. the Mobile Chargers) sothat all nodes have been charged efficiently while no locationin the field has electromagnetic radiation (EMR) exceedinga given threshold Rt.

The SCP is NP-hard. The main reasons are:

The EMR constraints are imposed on every point in thefield, which inevitably results in an infinite number ofconstraints.The objective function is non-convex which prevents theclassical optimization method to apply directly.

Representative paper:

Haipeng Dai, Yunhuai Liu, Guihai Chen, Xiaobing Wu and Tian He, “Safe

charging for wireless power transfer”, INFOCOM 2014


-3pt

..D2. Relevant TrendsEnergy Flow

There is a power access point which injects electricalpower and distributes it into the network in a form ofmulti-hop transfer.

This new type of multi-hop flow problem brings up newchallenges such as:

There is a new type of network flow problems, thepower flow problems that have to be addressed. Problem:multi-hop energy broadcast may reduce efficiency a lot!There are also the joint data and power flow problems thatneed investigation.


Liu Xiang, Jun Luo, Kai Han and Gaotao Shi, “Fueling Wireless Networks

perpetually: A case of multi-hop wireless power distribution”, PIMRC 2013


-3pt

..D3. Relevant TrendsAmbient Energy Harvesting

This technique converts the ambient energy from theenvironment into electricity to power the sensor nodes.

The renewable energy that is used to produce electricityincludes solar, wind, water and thermal energy.

Harvesting energy for low power devices (such as sensornodes) introduces new challenges since the energyharvesting device has to be small in size.

The main sources of ambient energy suitable for sensornetworks are:

solar energy,mechanical energy andthermal energy.


Winston K. G. Seah, Zhi Ang Eu and Hwee-pink Tan, “Wireless Sensor

Networks Powered by Ambient Energy Harvesting (WSN-HEAP) Survey

and Challenges”


-3pt

.. References I

C. M. Angelopoulos, S. Nikoletseas, D. Patroumpa and C. Raptopoulos,“Radiation-aware data propagation in wireless sensor networks”, 10th ACMSymposium on Mobility Management and Wireless Access, Paphos, Cyprus,(MOBIWAC 2012).

C. M. Angelopoulos, S. Nikoletseas, T. P. Raptis, C. Raptopoulos andF. Vasilakis, “Efficient energy management in wireless rechargeable sensornetworks”, 15th ACM International Conference on Modeling, Analysis andSimulation of Wireless and Mobile Systems, Paphos, Cyprus, (MSWiM2012).

C. M. Angelopoulos, S. Nikoletseas and T. P. Raptis, “Adaptive, limitedknowledge wireless recharging in sensor networks”, 11th ACM InternationalSymposium on Mobility Management and Wireless Access, Barcelona, Spain,(MobiWac 2013).

C. M. Angelopoulos, G. Filios, S. Nikoletseas, D. Patroumpa, T. P.Raptisand K. Veroutis, “A holistic IPv6 test-bed for smart, green buildings”, IEEEInternational Conference on Communications, Budapest, Hungary, (ICC2013).

H. Dai, Y. Liu, G. Chen, X. Wu and T. He, “Safe charging for wireless powertransfer”, The 33rd Annual IEEE International Conference on ComputerCommunications, Toronto, Canada (INFOCOM 2014).


-3pt

.. References II

H. Dai, L. Xu, X. Wu, C. Dong and G. Chen, “Impact of mobility on energyprovisioning in wireless rechargeable sensor networks”, IEEE WirelessCommunications and Networking Conference, Shanghai, China (WCNC2013).

A. Madhja, S. Nikoletseas and T. P.Raptis, “Efficient, distributedcoordination of multiple mobile chargers in sensor networks”, 16th ACMInternational Conference on Modeling, Analysis and Simulation of Wirelessand Mobile Systems, Barcelona, Spain, (MSWiM 2013).

A. Madhja, S. Nikoletseas and T. P.Raptis, “Hierarchical, collaborativewireless charging in sensor networks”, Technical report, 2014.

S. Nikoletseas, M. Rapti, T. P.Raptis, K. Veroutis, “Decentralizing andadding portability to an IoT test-bed through smartphones”, 2ndInternational Workshop on Internet of Things - Ideas and Perspectives,Marina Del Rey, CA, USA, (IoTIP 2014).

Y. Peng, Z. Li, W. Zhang and D. Qiao, “Prolonging sensor network lifetimethrough wireless charging”, 31st IEEE Real-Time Systems Symposium, SanDiego, California, USA (RTSS 2010).

W. K. G. Seah, Z.A. Eu and H. Tan, “Wireless Sensor Networks Powered byAmbient Energy Harvesting (WSN-HEAP) Survey and Challenges”.


-3pt

.. References III

L. Xiang, J. Luo, K. Han and G. Shi, “Fueling Wireless Networksperpetually: A case of multi-hop wireless power distribution”, 24th annualIEEE international symposium on personal, indoor and mobile radiocommunications. London, UK (PIMRC 2013).

L. Xie, Y. Shi, Y. T. Hou, W. Lou, H. D. Sherali, Hanif D. andS. F. Midkiff, “On renewable sensor networks with wireless energy transfer:The multi-node case”, 9th Annual IEEE Communications SocietyConference on Sensor, Mesh and Ad Hoc Communications and Networks,Seoul, Korea (SECON 2012).

S. Zhang, J. Wu and S. Lu, “Collaborative mobile charging for sensornetworks”, 9th IEEE International Conference on Mobile Ad hoc and SensorSystems, Las Vegas, Nevada, USA (MASS 2012)


-3pt

Thank You!


Documents

Wireless Energy Recharge Protocols for Mobile Crowdsensing in