Low-power and long-range communication technologies such as LoRa are becoming popular in IoT applications due to their ability to cover kilometers range with milliwatt of power consumption. One of the major drawbacks of LoRa is the data latency and the traffic congestion when the number of devices in the network increases. Especially, the latency arises due to the extreme duty cycling of LoRa end-nodes for reducing the overall energy consumption. To overcome this drawback, we propose a heterogeneous network architecture and an energy-efficient On-demand TDMA communication scheme improving both the device lifetime and the data latency of standard LoRa networks. We combine the capabilities of microwatt wake-up receivers to achieve ultra-low power states and pure asynchronous communication together with the long-range connectivity of LoRa. Experimental results show a data reliability of 100% and a round-trip latency on the order of milliseconds with end devices dissipating less than 46 mJ when active and 1.83 μW during periods of inactivity, lasting up to 3 years on a 1200 mAh Lithium battery.
Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open-source to the research community.
Radio communication remains the primary battery consuming activity in wireless systems. Advances in MAC protocols have enabled significant lifetime improvements, but in systems with low data rate, idle listening, and other communication artifacts can begin to dominate costs. One proposal to combat this is the addition of a second, extremely low power radio component that is always-on. As a consequence of the extremely low power, such radios are incapable of decoding general data, and thus are often delegated the task of listening for a trigger, leading to the terminology wake-up radio, as this extremely low power radio is used to wake up a higher power radio, which is then used for data communication. While wake-up technology has been steadily evolving over the last decade in the hardware arena, few protocols have been developed to exploit it. In this work, we present WaCo, our wake-up radio COOJA extension that allows exploration of the capabilities of the wake-up radio from the desktop environment. We also use our extended simulator to concretely show the potential benefits of the wake-up radio hardware with two, standard data collection protocols. Our results simultaneously confirm that wake-up technology has tremendous potential and that our simulator extension provides an effective mechanism for such exploration.
As a step toward sustainable wireless sensing, we present a proof of concept system that uses a Plant Microbial Fuel Cells (PMFC) as a power source. To match the very low power production capabilities of the PMFC, we couple it with an ultra-low power wake-up receiver used as a trigger for sampling and transmission of the sensed value. We demonstrate that this combination, with a new, receiver initiated MAC-level communication protocol, results in a sustainable system for reasonable data rates, shown to be 30s in our laboratory setting. This work offers the first steps toward large-scale wireless sensor networks in applications where the sensors are surrounded by living plants that can provide a green and perpetual power supply.
Smartphones with built-in sensors promise a conducive, objective way to quantify everyday body movements and classify those movements into activities. Utilizing smartphone accelerometer data we estimate the following daily activities performed by the user: walking, jogging, using stairs, sitting, standing and lying down. The proposal is tested experimentally via evaluations on real data obtained from 50 test users. The evaluation indicates that the J48 classifier using a window size of 512 samples with 50% overlapping yields the highest accuracy (i.e., up to 96.02%).
Smartphones equipped with various sensors provide sufficient sensor data and computation power to enable daily activity detection for applications such as u-healthcare, elderly monitoring, sports coaching and entertainment. Instead of applying multiple sensor devices, as observed in many previous investigations, this work proposes the use of a smartphone with its built-in accelerometer as an unobtrusive sensor device for real-time activity recognition of basic daily activities. The proposal is tested experimentally through evaluations on real data collected from 50 participants. A prototype application is developed to demonstrate and evaluate the selected classification methods for the designated recognition tasks. The results indicate that the J48 classifier using a window size of 512 samples with 50% overlapping obtained the highest accuracy (i.e., up to 96.02%). To measure the actual classification accuracy, a 5×10-fold cross-validation with different random seeds was performed on the dataset using WEKA. Finally, to determine whether a classifier is superior to another, 5×2 fold cross validation along with a paired t-test was subsequently performed on the results using J48 as the baseline scheme with the other classification algorithms being compared to it. A value of p<0.05 was considered statistically significant.
Smartphones with built-in sensors promise a convenient, objective way to evaluate everyday movements and recognize those movements into activities. Using accelerometer as a low-level sensor data we estimate the following daily activities performed by the user: walking, jogging, walking up stairs, walking down stairs, sitting and standing. Among five common machines learning algorithms: Decision Tree (J48), Naïve Bayes (NB), Support Vector Machines (SVM), Neural Network (NN), and Logistic Regression. NN classifier was found to be the best choice with the classification accuracy of more than 95%. It is shown that this method is appropriate and that the phone’s orientation information is not needed.
This paper presents a low cost and flexible home control and monitoring system using an embedded micro-web server, with IP connectivity for accessing and controlling devices and appliances remotely using Android based Smart phone app. The proposed system does not require a dedicated server PC with respect to similar systems and offers a novel communication protocol to monitor and control the home environment with more than just the switching functionality.
This paper presents an extensible and flexible architecture for integrating Wireless Sensor Networks with the Cloud. REST-based Web services is used as an interoperable application layer that can be directly integrated into other application domains for remote monitoring such as e-health care services and smart environments. For proof of concept, we have set up a REST-based Web services on an IP-based low power WSN testbed, which enables data accessibility from anywhere. The alert feature has also been implemented to notify users via email or tweets for monitoring data when they exceed values and events of interest.
This paper represents the design, implementation, and experimental results of a Radio Frequency (RF) based wireless control of a distributed Peripheral Interface Controller (PIC) microcontroller based Automated Guided Vehicle (AGV), which is known as ROVER II (Roaming Vehicle for Entity Relocation). ROVER II was designed in-house as a general purpose guide path following mobile platform for material handling and transportation within a manufacturing facility.
Technology is a never ending process. To be able to design a product using the current technology that will be beneficial to the lives of others is a huge contribution to the community. This paper presents the design and implementation of a low cost but yet flexible and secure cell phone based home automation system. The design is based on a stand alone Arduino BT board and the home appliances are connected to the input/ output ports of this board via relays. The communication between the cell phone and the Arduino BT board is wireless. This system is designed to be low cost and scalable allowing variety of devices to be controlled with minimum changes to its core. Password protection is being used to only allow authorised users from accessing the appliances at home.