Secure and Reliable Power ConsumptionMonitoring in Untrustworthy Micro-grids

Pacome Ambassa1, Anne Kayem 1, Stephen Wolthusen 2

Christoph Meinel 3

1Department of Computer Science 2NISlab 3Hasso Plattner InstituteUniversity of Cape Town Department of Computer Science University of Potsdam

South Africa Gjøvik University College, Norway Germanypambassa, akayem@cs.uct.ac.za stephen.wolthusen@hig.no meinel@hpi.de

International Conference on Future Network Systems and Security (FNSS 2015)June 13, 2015 Paris, France

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Outline

1 Introduction

2 Related Work

3 System Description

4 Asynchronous Collection of Household Power ConsumptionData

5 Noise Characterization in Power Consumption Data

6 Conclusion

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Introduction

Introduction: Context

Low income communities in developing countries:

Computational limitationsIntermittent network connectivityUnstable power connectivity

Do not have reliable access to electricity

Not connected to national power networksAccess negatively influenced by load shedding

Governments, private developers and NGOs could setup aMicro-grids for power sharing.

Challenge: Generation does not satisfy demand

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Introduction

Introduction: Motivation

Design effective and efficient micro-grids architecture...

Re-modeled the power network to incorporate

Incorporate portable and cheap information and communicationtechnologyMobile computing devices – popular in developing countriesSensorsWireless communication technology.

Reliability and trust properties: critical for grid stability

Reliability: Fair access to the network amongst the Stakeholder,Trust

Limited computational system enable power network monitoringDetermine power consumptionEnsure reliable operation of the networkstate estimations : precondition for grid stability

Integrity of data guide the power distributionAmbassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 4 / 17

Related Work

Related Work

Power network monitoringConventional power network monitoring solutions are based onutilizing smart meters and trustworthy calibrated sensor installedinto home networks for consumption monitoring.

They either don’t make any assumption on the aggregationprocess or assume a synchronized system

Monitoring and state estimation in distributed systemCentered on snapshot algorithms

Snapshot algorithm for fully connected network, reliablecommunication channel and FIFO message ordering.

Most are not suitable for network with limitation on computationbecause of high communication overhead

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System Description

Micro-Grid network

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Asynchronous Collection of Household Power Consumption Data

Household Network

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Asynchronous Collection of Household Power Consumption Data

Notations

Let A the set of all appliances within the house, n = |A |.Aj the set of active devices, Aj ⊆ A and j ∈ [1,p].

The set of sensors s1,s2, . . . ,sn installed to monitor homeappliances power consumption.

M mobile device represents base station /sink/aggregation point

Network modelsystem can be modeled by an undirected and connected graphG = (S,E), where S is the set of nodes in the networks and E is aset of communication links among the nodes in S

G is the communication graph of this WSN.

Two nodes si and sj are connected if and only if si communicatesdirectly with sj . si and sj are neighbors

The set N (si) is the set of vertices adjacent to si .

Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 8 / 17

Asynchronous Collection of Household Power Consumption Data

Notations

Let A the set of all appliances within the house, n = |A |.Aj the set of active devices, Aj ⊆ A and j ∈ [1,p].

The set of sensors s1,s2, . . . ,sn installed to monitor homeappliances power consumption.

M mobile device represents base station /sink/aggregation point

Network modelsystem can be modeled by an undirected and connected graphG = (S,E), where S is the set of nodes in the networks and E is aset of communication links among the nodes in S

G is the communication graph of this WSN.

Two nodes si and sj are connected if and only if si communicatesdirectly with sj . si and sj are neighbors

The set N (si) is the set of vertices adjacent to si .Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 8 / 17

Asynchronous Collection of Household Power Consumption Data

Challenge

Data collection in a distributed communication network under thefollowing conditions:

1 Lack of globally shared clock between different nodes(synchronization problems)

2 Unpredictable communication latency3 Power consumption values are spread across several appliances4 Nodes and link are susceptible to failure5 The presence of network adversaries : (data modification attack,

denial of service attacks)

ProblemRecording and for collection of power consumption data inasynchronous networks.

Similar to the computation problem of recording the in global statedistributed system

Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 9 / 17

Asynchronous Collection of Household Power Consumption Data

Challenge

Data collection in a distributed communication network under thefollowing conditions:

1 Lack of globally shared clock between different nodes(synchronization problems)

2 Unpredictable communication latency3 Power consumption values are spread across several appliances4 Nodes and link are susceptible to failure5 The presence of network adversaries : (data modification attack,

denial of service attacks)

ProblemRecording and for collection of power consumption data inasynchronous networks.

Similar to the computation problem of recording the in global statedistributed system

Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 9 / 17

Asynchronous Collection of Household Power Consumption Data

Challenge

Data collection in a distributed communication network under thefollowing conditions:

1 Lack of globally shared clock between different nodes(synchronization problems)

2 Unpredictable communication latency3 Power consumption values are spread across several appliances4 Nodes and link are susceptible to failure5 The presence of network adversaries : (data modification attack,

denial of service attacks)

ProblemRecording and for collection of power consumption data inasynchronous networks.

Similar to the computation problem of recording the in global statedistributed system

Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 9 / 17

Asynchronous Collection of Household Power Consumption Data

Snapshot Algorithm: A solution for Global State collection

The distributed snapshot produce a global state of a DS

Collection of local states of process Pi .Collection of the channel state .

The state of process Pi is the content of processors, register, stackand memory

The state of the channel is characterize by the set of message intransit

A global state corresponds to the entire household’s energyconsumption compute from per appliance consumption.

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Asynchronous Collection of Household Power Consumption Data

The Proposed Snapshot Algorithm

Marker: the control message that informs the sensor node torecord the value(s) measured. It contains: sid , the ID of the sendernode; and snapnumb, the snapshot number.

Feedback: the message sent by a sensor to the sink node. Itcontains: sid , identifier of the sender node; Nsnd , the new valuerecorded; snapnumb an integer which indicates the snapshot; andMid , the ID of the sink node.

lmd : a real number which is the reading of the sensor at a givenpoint in time.

Osnd : the old value collected in the previous snapshot

flag: A Boolean value that indicates if a sensor node has receivedthe marker.

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Asynchronous Collection of Household Power Consumption Data

The Proposed Algorithm

¶ Assume a spanning tree for communication [Li et al, 2005]· Three steps algorithm:

ä Snapshot initiationä Reception of Markerä Feedback response

Phase 1: Snapshot initiationThe mobile device broadcast Marker (sid ,snapnumb) over a spanningtree initiate the collection

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Asynchronous Collection of Household Power Consumption Data

The Proposed Algorithm

¶ Assume a spanning tree for communication [Li et al, 2005]· Three steps algorithm:

ä Snapshot initiationä Reception of Markerä Feedback response

Phase 2: Reception of MarkerUpon receiving the marker message, Marker (sid ,snapnumb), thereceiver (an adjacent neighbor sj ∈ N (si) first check the flag value.

If the value of flag is false, sj has not yet received the marker thenit records its current readings lmd .

sj broadcast the control message Marker (sj ,snapnumb) to itsadjacent neighbor.

Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 12 / 17

Asynchronous Collection of Household Power Consumption Data

The Proposed Algorithm

¶ Assume a spanning tree for communication [Li et al, 2005]· Three steps algorithm:

ä Snapshot initiationä Reception of Markerä Feedback response

Phase 3: Feedback response

If Nsnd 6= Osnd send Feedback with (sid ,Nsnd ,snapnumb,Mid ) .

Osnd ← Nsnd .

Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 12 / 17

Noise Characterization in Power Consumption Data

Noise in Power Data

Noisy in measured data are due to:Errors from the physicalmeasurement and Malicious measurements

1 Errors from the physical measurement (measurement errors):The difference between the measured value and the true valueLet u be the true value, x be the measured value and β be themeasurement error. Then, β = x−u or u = x−β.Three different types of measurement errors: systematic errors,random errors and negligent errors

2 Malicious measurements: false data injection:Random false data injection attacksTargeted false data injection attacks

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Noise Characterization in Power Consumption Data

Measurement Errors

1 Systematic errors

Result from imperfections of the metering equipment, inexactadjustment and pre-settingsNo statistical techniques to quantify systematic errors[Hughes,2010]

2 Random errors

The reading of si taken at different time fluctuates.The combination of such tiny perturbations is represented as arandom variable XX follow Gaussian distributions.

3 Negligent errors

Result from mistakes or a malfunction of the measuring device

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Noise Characterization in Power Consumption Data

Malicious measurements: false data injection

Maliciously inject bad measurement into the data stream in orderto misreport consumption

Two attacks scenarios [Liu,2009]

Random data injection attacks

Targeted data injection attacks.

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Conclusion

Conclusion

Most of the daily activity are electricity dependent

Framework for a cost efficient micro grid architecture for powerdistribution in low resource environmentEfficient distributed snapshot algorithm for power consumptioncollection in an asynchronous and distributed network

Message complexity is O(N) in a network with N nodes

Characterization of noise in data collection

On-going work: demand load management over distributednetwork as a method of scheduling to optimize power consumptionin such a ways to guarantee grid stability

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Thank for your kind attention !!!Ambassa, Kayem, Wolthusen & Meinel (UCT, HIG & HPI) Power monitoring in a micro-grid 17 / 17