Ranveer Chandra (Microsoft Research) Low Cost and Secure Smart Meter Communications using the TV White Spaces Omid Fatemieh (UIUC) Ranveer Chandra (Microsoft Research) Carl A. Gunter (UIUC)

Advanced Meter Infrastructure (AMI) AMI: integral part of smart grid Reconfigurable nature and communication capabilities of advanced (smart) meters allow for deploying a rich set of applications Automated meter reading Outage management Demand response Electricity theft detection Support for distributed power generation

Existing AMI Communications ISM bands Crowded in urban areas Short distances not suitable for rural areas Cellular links Expensive and low bandwidth Crowded in urban areas and limited in rural areas Proprietary mesh network technology reduces inter-operability and impedes meter diversity Idea: Use white spaces for AMI communications Propose a secure architecture that yields benefits in cost, bandwidth, and deployment Design architecture such that high bandwidth and long transmission ranges yield benefits in cost, bandwidth, and deployment

White Spaces White spaces are unused portions of TV spectrum (54-698 MHz) Excellent long-range communication and penetration properties FCC’s recent rulings (Nov ‘08, Sep ‘10) allows for unlicensed communication in white spaces Spectrum sensing helps with identifying and assessing quality of unused channels Standards and research prototypes IEEE 802.22 Wireless Broadband Regional Area Network Point to multipoint architecture Typical range: 17 - 33 km (but up to 100 km) WhiteFi [BahlCMMW09 - Sigcomm ‘09] Wi-Fi like connectivity over white spaces for up to 2km Adaptively operates in most efficient chunk of available spectrum Both require centrally aggregating spectrum sensing data Several scenarios in white space networks require the aggregation of spectrum sensing data. For example: -In order to form a network over the white spaces, the CRs need to periodically report sensing results to a base station. The base station is in charge of collecting the readings from the CRs and determining the areas of primary presence. This centralized approach has been endorsed by the IEEE 802.22 WRAN standard draft [3], CogNeA [1] and recent research publications [9]. - Collaborative sensing refers to the process of combining spectrum sensing results from cognitive radios for the purpose of primary detection. The main benefit of this approach is the mitigation of multi-path fading and shadowing effects, which improves the detection accuracy in highly shadowed environments [20]. In addition, it allows for relaxation of sensitivity requirements at individual CRs [45]. - Crowdsourcing of spectrum reports from white space devices can be used to build a nationwide database of spectrum availability. Such a database can be used to augment the white space geolocation database mandated by the FCC [2] or to learn spectrum usage as part of the recently passed Spectrum Inventory Bill [4].

Proposed Architecture Utility operates WhiteFi networks Utility buys service from independent 802.22 service provider Large number and geographical spread of meters -> great for spectrum sensing -> utility can offer data to 802.22 provider

Benefits High data rates (at low cost) Single hop from meters to WhiteFi base station No need for complex meshes Saves energy used in mesh maintenance and routing Large base of sensors for the 802.22 provider Lowers cost for 802.22 service provider Lowers 802.22 service cost for utility Lowers cost for providing broadband service to rural areas Facilitates additional meters deployments in rural areas (particularly along power lines) Saves energy used in mesh maintenance and routing

Challenges and Security Considerations Cost of equipping meters with CRs and antennas Will be lowered with large-scale production May be lowered for utility by contract with 802.22 provider Limited availability of white-spaces Unlikely in rural and suburban areas Can use ISM or narrow licensed bands as backup Primary emulation / unauthorized spectrum usage attacks Transmitter localization [ChenPR – JSAC ‘08], Anomaly detection [LiuCTG09 - Infocom ‘09], Signal authentication [LiuND10 - Oakland ‘10] Malicious false spectrum sensing report attacks Vandalism: falsely declare a frequency as free Exploitation: falsely declare a frequency as occupied

Detecting False Reports Particularly important for AMI Errors will disrupt AMI communication 802.22 provider cannot only rely on meters Meters owned by a different entity (utility) Meters may not be well-distributed, or get compromised Must use additional sources for spectrum sensing: mobile units, consumer premise equipment, or deployed sensors Sensors have unknown integrity and or get compromised Detecting false reports Based on propagation models [FatemiehCG – DySPAN ‘10] Based on propagation data [FatemiehFCG – NDSS ‘11] O. Fatemieh, A. Farhadi, R. Chandra, C. A. Gunter, Using Classification to Protect the Integrity of Spectrum Measurements in White Space Networks, NDSS 2011. Summary: A key enabling technology for forming networks over the TV white-spaces is the aggregation of spectrum availability data from multiple sources. However, this aggregation is vulnerable to maliciously misreported measurements. We propose a technique that uses trusted propagation data to build an SVM classifier, which is subsequently used to detect violations. Our work eliminates the need for arbitrary assumptions about propagation models and parameters. Evaluations using FCC and NASA data show our technique is effective.

Data-based (Classification-based) Detection Model-based schemes: not clear which signal propagation models, parameters, or outlier thresholds should be used Idea: Let data speak for itself Provide natural and un-natural signal propagation patterns to train a machine learning SVM classifier Subsequently use classifier to detect unnatural propagation patterns -> attacker-dominated cells FatemiehFCG – NDSS 2011

Evaluation Transmitter data from FCC Terrain data from NASA Flat East-Central Illinois Hilly Southwest Pennsylvania (Stress Test) Transmitter data from FCC Terrain data from NASA House density data from US Census Bureau FatemiehFCG – NDSS 2011

Pennsylvania Stress Test Results 20km by 20km area Data from 37 transmitters in 150km radius Train using data from 29 Test on the data from 8 Represent un-accounted fading and other signal variations: add Gaussian variations with mean 0 and std. dev up to 6 (dB-Spread) only to test data FatemiehFCG – NDSS 2011

Summary AMI communications a key part of smart grid Proposed communication architecture that offers improvements in bandwidth, deployment, and cost Discussed security and reliability challenges Identified exploitation/vandalism as important attacks and proposed techniques to detect them References O. Fatemieh, R. Chandra, C. A. Gunter, Low Cost and Secure Smart Meter Communications using the TV White Spaces, ISRCS ’10. O. Fatemieh, R. Chandra, C. A. Gunter, Secure Collaborative Sensing for Crowdsoucing Spectrum Data in White Space Networks, DySPAN ’10. O. Fatemieh, A. Farhadi, R. Chandra, C. A. Gunter, Using Classification to Protect the Integrity of Spectrum Measurements in White Space Networks, NDSS ’11.

Backup

Standards and Research Prototypes for White-Space Communications IEEE 802.22 standard draft Wireless broadband regional area networks over TV white spaces Point to multipoint architecture (base station to up to 255 clients), with the possibility of having repeaters in between Each access point covers 17 - 33 km (typical) but up to 100 km Antennas 10m above the ground, similar to TV antennas Support for co-existence between cells WhiteFi [BahlCMMW09 - Sicgomm ‘09] Wi-Fi like connectivity over white spaces for up to 2km Adaptively operates in most efficient contiguous chunk of available spectrum Client to access point communication: using modified stock Wi-Fi cards Requires a separate antenna and board for spectrum sensing For spectrum allocation, both techniques support spectrum sensing and using transmitter databases