Improving Radio Energy Harvesting in Robots using Mobility Diversity

In this paper, we propose a new technique that exploits a robot's (intelligently) controlled mobility to maximize stored radio energy. In particular, we examine a scenario where the mobile robot takes a break from its normal activity for a duration of T s. This “dead time” consists of three phases-searching, positioning, and resting-which ensure that the robot can optimize its energy harvesting from a base station transmitting a narrowband RF signal over a flat-fading wireless channel. We utilize the mobility diversity principle, which arises due to the spatial wireless channel diversity experienced by motion of the robot. By optimal exploitation of the small scale fading, we maximize the net amount of energy (i.e., the energy harvested by the robot minus the mechanical energy used for motion) that the robot stores over the “dead time”. To the best of the authors' knowledge, this paper is the first use of the mobility diversity principle to optimize energy harvesting from an RF signal. We demonstrate that mobility, if intelligently controlled, is actually not a foe but is indeed a friend that can provide significant benefits under wireless fading channels. Through simulations, we verify the analytical results and illustrate the improvement in the energy stored compared with not using intelligent mobility. Finally, we show that the efficiency of our approach is clearly coupled with various design parameters, including the center frequency of the narrowband RF signal and the duration of the “dead time”

Facilitating Internet of Things on the Edge

The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment. Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management. The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190