Cisco Technology, Inc.

Abhishek Bhattacharyya

Abhishek Mukherji

Santosh Ghanshyam Pandey

Huy Phuong Tran

Vinay S Raghuram

Prasad Walawalkar

Balaji Gurumurthy

Jagdish Girimaji

Rahul Dasgupta

Matthew Aaron Silverman

Anand K Oswal

Santosh Ramrao Patil

Nancy Cam-Winget

Sivadeep Reddy Kalavakuru

Preethi Natarajan

Damodharam Ammepalli

Xiaohua Fang

Jenny Marie Yoshihara

Chayan Bhaisare

A method includes generating, at a server, an event policy for controlling one or more wireless beacon devices in a network; detecting an event in the network; determining whether the event matches the event policy; when the event matches the event policy, generating programming information for configuring the one or more wireless beacon devices; and forwarding the programming information via one or more wireless access points to the one or more wireless beacon devices for configuring the one or more wireless beacon devices based on the programming information.

Event-driven policy based management of wireless beacon and tag devices

Various embodiments herein disclose improving battery life while determining a location of a Bluetooth device. A corresponding method comprises receiving an identifier for a Bluetooth Low Energy (BLE) device to locate in an environment comprising a plurality of access points (APs). The method further comprises identifying a first AP of the plurality of APs communicating with the BLE device at a first power level. The method also comprises instructing, using the first AP, the BLE device to increase a transmission power level of the BLE device until a threshold number of APs of the plurality of APs, including the first AP, receive communications from the BLE device. The method additionally comprises receiving an RSSI value from each of the threshold number of APs based on the communications from the BLE device. The method also comprises computing a location of the BLE device in the environment based on the RSSI values.

Conserving battery life during Bluetooth device localization

Location-based, context-aware challenge-response authentication may be provided. First, a challenge may be provided to a user. The challenge may be based on a context corresponding to the user. The context corresponding to the user may comprise a location of a device associated with the user within an environment. Next, in response to providing the challenge, a response to the challenge may be received from the user. Then, in response to receiving the response to the challenge, it may be determined that the response is a correct answer to the challenge. In response to determining the response is the correct answer, a privilege may be provided to the user.

Location-based, context-aware challenge-response authentication

In accordance with one aspect, presented herein is a method to encrypt beacon device telemetry broadcast packets while respecting the low power and low processing requirements inherent to wireless beacon devices and various other challenges which such an encryption scheme brings. In accordance with another aspect, a methodology is provided through which the network can identify if an unauthorized connection is being established with a beacon device and thereby prevent potential beacon device tampering.

Enabling secure beacon telemetry broadcasts based on battery power state of a beacon device

An access point includes first and second radios that operate in accordance with first and second wireless protocols, respectively. The access point performs a method including receiving a first protocol packet of the first wireless protocol and a second protocol packet of the second wireless protocol transmitted by a wireless device over a communication channel, and measuring a first received power of the first protocol packet and a second received power of the second protocol packet at the access point. The method further includes determining whether reception times of the first protocol packet and the second protocol packet are within a coherence time for the communication channel and, when the reception times are within the coherence time, combining the first received power and the second received power into a multiprotocol received power. The method also includes determining a location of the wireless device based on the multiprotocol received power.

Multi-protocol concurrent RSSI and AoA for location computation

In one embodiment, a traffic analysis service receives payload data from packets sent by a sensor tag in a network. The service forms a payload signature for the sensor tag, based on the payload data. The payload signature is indicative of one or more bytes in the payload that vary across the packets. The service identifies a portion of the payload data as potentially including a sensor measurement, based on the payload signature. The service uses a machine learning classifier to assign a sensor measurement type to the identified portion of the payload data.

Vendor agnostic sensor telemetry detection, processing, and identification

In one embodiment, a device receives location estimates for a wireless node in a network, each location estimate having an associated timestamp. The device applies hierarchical clustering to the received location estimates and their associated timestamps, to identify locations and points in time in which the wireless node was stationary. The device performs sequence modeling on the identified locations and points in time in which the wireless node was stationary, to form a sequence of locations and associated time periods in which the wireless node was stationary. The device associates the wireless node with a behavioral profile based on the sequence of locations and associated time periods in which the wireless node. The device generates, based in part on the behavioral profile for the wireless node, a predictive model that predicts a location of the wireless node at a particular point in time.

Characterizing movement behaviors of wireless nodes in a network

An efficient process is provided that exploits features in historical and current client location data to forecast client counts of different zones up to several hours ahead. These features may be obtained from correlations of client counts of multiple zones in a recent and long period of time. These features may also be combined using techniques that choose the best performing method for a particular dataset and a particular lookahead time. This process provides better forecast/prediction on the zone-based client count data, and is very useful in customer analytics which can now show the future predicted value. This can help the analytics customers to plan their operations based on the location analytics.

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Fremont, CA, United States of America

Abhishek Bhattacharyya