braj De

SmartGridLab: A Laboratory-Based Smart Grid Testbed

The evolution of traditional electricity grid into a state-of-the-art Smart Grid will need innovation in a number of dimensions: seamless integration of renewable energy sources, management of intermittent power supplies, realtime demand response, energy pricing strategy etc. The grid configuration will change from the central broadcasting network into a more distributed and dynamic network with two-way energy transmission. Information network is another necessary component that will be built on the power grid, which will measure the status of the whole power grid and control the energy flow. In this perspective of unsolved problems, we have designed SmartGridLab, an efficient Smart Grid testbed to help the research community analyze their designs and protocols in lab environment. This will foster the Smart Grid researchers to develop, analyze and compare different designs conveniently and efficiently. Our designed testbed consists of following major components: Intelligent Power Switch, power supply (main supply and renewable energy supply), energy demander (e.g. appliance), and an information network containing Power Meter. We have validated the usage of our designed testbed for greater research problems in Smart Grid.

TelosW: Enabling ultra-low power wake-on sensor network

Sensor networks are typically sensor or radio event driven. Exploiting this property we propose a novel wakeon sensor network design. In this context we have designed a new sensor platform called TelosW. The wake-on sensing capability of TelosW lets designated sensors wake up the microcontroller(MCU) only on occurrence of some event with preconfigurable threshold. TelosW also includes the CC1101 [3] Wake-On Radio (WOR) hardware that performs low power listening without intervention of MCU. These all lead to a completely event driven wake-on sensor network that reduces energy consumption considerably. TelosW is also equipped with an on-board energy meter that can precisely measure in-situ energy consumption. Using the energy meter it is possible to get the insight of energy states of nodes in a network at any time. This makes it possible to practically analyze energy-efficient protocols. The experiments show that the energy consumption has been significantly reduced comparing to same application without wake-on design.

FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments

In this paper we have proposed and designed FindingHuMo (Finding Human Motion), a real-time user tracking system for Smart Environments. FindingHuMo can perform device-free tracking of multiple (unknown and variable number of) users in the Hallway Environments, just from non-invasive and anonymous (not user specific) binary motion sensor data stream. The significance of our designed system are as follows: (a) fast tracking of individual targets from binary motion data stream from a static wireless sensor network in the infrastructure. This needs to resolve unreliable node sequences, system noise and path ambiguity, (b) Scaling for multi-user tracking where user motion trajectories may crossover with each other in all possible ways. This needs to resolve path ambiguity to isolate overlapping trajectories, FindingHumo applies the following techniques on the collected motion data stream: (i) a proposed motion data driven adaptive order Hidden Markov Model with Viterbi decoding (called Adaptive-HMM), and then (ii) an innovative path disambiguation algorithm (called CPDA). Using this methodology the system accurately detects and isolates motion trajectories of individual users. The system performance is illustrated with results from real-time system deployment experience in a Smart Environment.

Survey of Security Advances in Smart Grid: A Data Driven Approach

With the integration of advanced computing and communication technologies, smart grid is considered as the next-generation power system, which promises self healing, resilience, sustainability, and efficiency to the energy critical infrastructure. The smart grid innovation brings enormous challenges and initiatives across both industry and academia, in which the security issue emerges to be a critical concern. In this paper, we present a survey of recent security advances in smart grid, by a data driven approach. Compared with existing related works, our survey is centered around the security vulnerabilities and solutions within the entire lifecycle of smart grid data, which are systematically decomposed into four sequential stages: 1) data generation; 2) data acquisition; 3) data storage; and 4) data processing. Moreover, we further review the security analytics in smart grid, which employs data analytics to ensure smart grid security. Finally, an effort to shed light on potential future research concludes this paper.

Efficient aerial data collection with UAV in large-scale wireless sensor networks

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data.

A wireless smart grid testbed in lab

State-of-the-art Smart Grid design needs innovation in a number of dimensions: distributed and dynamic network with two-way information and energy transmission, seamless integration of renewable energy sources, management of intermittent power supplies, realtime demand response, and energy pricing strategy. To realize these, we have designed SmartGridLab, a wireless Smart Grid testbed to help the Smart Grid research community analyze and evaluate their designs and developed protocols in a lab environment.

A wake-on sensor network

This paper present a wake-on sensor network formed with the wake-on motes, TelosW. Our wake-on hardware and software design enable lower power operations and longer network lifetime.

Trajectory mining from anonymous binary motion sensors in Smart Environment

One of the key applications of Smart Environment (which is deployed with anonymous binary motion sensors [1,2]) is user activity behavior analysis. The necessary prerequisite to finding behavior knowledge of users is to mine trajectories from the massive amount of sensor data. However, it becomes more challenging when the Smart Environment has to use only non-invasive and binary sensing because of user privacy protection. Furthermore, the existing trajectory tracking algorithms mainly deal with tracking object either using sophisticated invasive and expensive sensors [3,4], or treating tracking as a Hidden Markov Model (HMM) which needs adequate training data set to obtain models parameter [5]. So, it is imperative to propose a framework which can distinguish different trajectories only based on collected data from anonymous binary motion sensors. In this paper, we propose a framework - Mining Trajectory from Anonymous Binary Motion Sensor Data (MiningTraMo) - that can mine valuable and trust-worthy motion trajectories from the massive amount of sensor data. The proposed solution makes use of both temporal and spatial information to remove the system noise and ambiguity caused by motion crossover and overlapping. Meanwhile, MiningTraMo introduces Multiple Pairs Best Trajectory Problem (MPBT), which is inspired by the multiple pairs shortest path algorithm in [6], to search the most possible trajectory using walking speed variance when there are several trajectory candidates. The time complexity of the proposed algorithms are analyzed and the accuracy performance is evaluated by some designed experiments which not only have ground truth, but also are the typical situation for real application. The mining experiment using real history data from a smart workspace is also finished to find the users behavior pattern.

ActiSen: Activity-aware sensor network in smart environments

A sensor network, unlike a traditional communication network, provides high degree of visibility into environmental physical processes. Therefore its operation is driven by the activities in the environment. In long-term operations, these activities usually show certain patterns which can be learned and utilized to optimize network design. However, this has been almost unexplored in the literature. In this paper we present the design and validation of ActiSen system, an activity-aware sensor network in smart environments. ActiSen consists of three components: activity-aware sensing, activity-aware radio duty-cycling, and activity-aware and energy balanced routing. Experimental results from real testbed experiments and sensor network simulator TOSSIM validate that the activity-aware design of ActiSen outperforms existing methods in terms of resource utilization (energy efficiency, lifetime etc.) and system performance (data delivery throughput, delivery latency etc.).

EAR: An Energy and Activity-Aware Routing Protocol for Wireless Sensor Networks in Smart Environments

Sensor network, unlike traditional communication network, is deeply embedded in physical environments and its operation is mainly driven by the event activities in the environment. In long-term operations, the event activities usually show certain patterns which can be learned and exploited to optimize network design. However, this has been underexplored in the literature. One work related to this is using ATPG for radio duty cycling ([1]). In this paper we present a novel Energy and Activity aware Routing (EAR) protocol for sensor networks. As a case study, we have evaluated EAR with the data trace of real Smart Environments. In EAR an Activity Transition Probability Graph (ATPG) is learned and built from the event activity patterns. EAR is an online routing protocol, which chooses the next-hop relay node by utilizing: activity pattern information in the ATPG graph and a novel index of energy balance in the network. EAR extends network lifetime by maintaining an energy balance across the nodes in the network, while meeting application performance with desired throughput and low data delivery latency. We theoretically prove that: (a) the network throughput with EAR achieves a competitive ratio (i.e., the ratio of the performance of any offline algorithm that has knowledge of all past and future packet arrivals to the performance of our online algorithm) which is asymptotically optimal, and (b) EAR achieves a lower bound in network lifetime. Extensive experimental results from: (a) 82 node Motelab sensor network testbed [2] and (b) varying size network (20-100) in sensor network simulator TOSSIM, validate that EAR outperforms the existing methods both in terms of network performance (network lifetime, network energy consumption) and application performance (low latency, desired throughput) for an energy-constrained sensor network.