SenseLE:Exploiting spatial locality in decentralized sensing environments

Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%

Large scale stream analytics using a resource-constrained edge

A key challenge for smart city analytics is fast extraction, accumulation and processing of sensor data collected from a large number of IoT devices. Edge computing has enabled processing of simple analytics, such as aggregation, geographically closer to the IoT devices to improve latency. However, the throughput of processing in the edge depends on the type of resources available, the number of IoT devices connected and the type of stream analytics performed in the edge. We introduce a framework called Seagull for building efficient, large scale IoT-based applications. Our framework distributes the stream analytics processing tasks to the nodes based on their proximity to the sensor data source as well as the amount of processing the nodes can handle. Our evaluation shows the effect of various stream analytics parameters on the maximum sustainable throughput for a resource-constrained edge device

Aves: A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices

Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves - a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine's improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications