COIN: Opening the internet of things to people's mobile devices

People's interaction with IoT devices such as proximity beacons, body-worn sensors, and controllable light bulbs is often mediated through personal mobile devices. Current approaches usually make applications operate in separate silos, as the functionality of IoT devices is fixed by vendors and typically accessed only through low-level proprietary APIs. This limits the flexibility in designing applications and requires intense wireless interactions, which may impact energy consumption. COIN is a system architecture that breaks this separation by allowing developers to flexibly run a slice of a mobile app's logic onto IoT devices. Mobile apps can dynamically deploy arbitrary tasks implemented as loosely coupled components. The underlying runtime support takes care of the coordination across tasks and of their real-time scheduling. Our prototype indicates that COIN both enables increased flexibility and improves energy efficiency at the IoT device, compared to traditional architectures

Characterizing Multi-radio Energy Consumption in Cellular/Wi-Fi Smartphones

Cellular networks evolved to meet the ever increasing traffic demand by way of offloading mobile traffic to Wi-Fi network elements. Exploiting multi-radio interfaces on a smartphone has recently been examined with regards to heterogeneous bandwidth aggregation and radio switching. However, how a smartphone consumes its energy in driving cellular and Wi-Fi multi-radio interfaces, is not well understood. In this paper, we revealed the energy consumption behavior of 3G cellular and Wi-Fi multi-radio operations of a smartphone. We modified smartphone’s firmware to enable multi-radios operations simultaneously and we performed extensive measurements of multi-radio energy consumption in a real commercial network. From the measured data set, we established a realistic multi-radio energy consumption model and it gave 98% stability from the derived coefficients

Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms |, |, |, |, |, |, and |. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.U01 OH010841/OH/NIOSH CDC HHSUnited States/U54 OH007544/OH/NIOSH CDC HHSUnited States

Performance of short and long range wireless communication technologies in construction

The ever increasing complexity of construction projects asks for improved communication and automated data collection supported by continually improving electronic tools. Advances in information technologies enable us to link critical resources on construction sites, such as trucks and cranes, to the project website creating many opportunities to drastically improve productivity, safety and quality. While the use of electronic equipment is nothing new in construction, no model exists to integrate them into one unified framework. This paper presents a wireless site-network concept consisting of information hubs enabled to automatically connect data sinks with sources supported by software agents. Included in this paper is the discussion of a mobile information hub, the eCKiosk, enabled to connect the work crew electronically to the project network while collecting automatically live “as-built” data. It begins with a review of long range wireless as the basis for designing a robust Agile Site Communication Network (ASCNet). Site experiments with short range wireless conduits and embedded RFID tags showed that they are able to provide information far beyond an identification number. While wireless technologies are poised to open totally new avenues to manage construction, more field-tests are needed to establish a solid knowledge base to create a pervasive network for the dynamically changing building site

Profiling Power Consumption on Mobile Devices

The proliferation of mobile devices, and the migration of the information access paradigm to mobile platforms, motivate studies of power consumption behaviors with the purpose of increasing the device battery life. The aim of this work is to profile the power consumption of a Samsung Galaxy I7500 and a Samsung Nexus S, in order to understand how such feature has evolved over the years. We performed two experiments: the first one measures consumption for a set of usage scenarios, which represent common daily user activities, while the second one analyzes a context-aware application with a known source code. The first experiment shows that the most recent device in terms of OS and hardware components shows significantly lower consumption than the least recent one. The second experiment shows that the impact of different configurations of the same application causes a different power consumption behavior on both smartphones. Our results show that hardware improvements and energy-aware software applications greatly impact the energy efficiency of mobile device