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This article presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a node's average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms. Based on our models we present a MAT-LAB Wireless Sensor Node Platform Lifetime Prediction and Simulation Package (MATSNL). This demonstrates the use of the models using sample applications drawn from existing sensor node measurements.

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person's mobile phones. Since a person's motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones' IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a twomeasurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed.

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Abstract-The ability to localize and identify multiple people is paramount to the inference of high-level activities for informed decision-making. In this paper, we describe the PEM-ID system, which uniquely identifies people tagged with accelerometer nodes in the video output of preinstalled infrastructure cameras. For this, we introduce a new distance measure between signals comprised of timestamps of gait landmarks, and utilize it to identify each tracked person from the video by pairing them with a wearable accelerometer node.

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Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded wireless power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy . This article exploits the compression and encryption properties of compressive sensing (CS) to design a jo<underline>i</underline>nt data <underline>c</underline>ompression and <underline>e</underline>ncryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and space complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features an adaptive reconfiguration mechanism that selects the signal representation basis of CS at runtime among several candidate bases to achieve the best fidelity of the recovered data at the gateways. This mechanism enables JICE to adapt to changing power consumption patterns. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, at a meter sampling rate of 8 Hz, JICE increases the number of meters supported by a gateway by 50%, compared with the commonly used pipeline approach, while keeping a signal distortion rate lower than 5%.