Power-Positive Networking: Wireless-Charging-Based Networking to Protect Energy Against DoS Attacks
Ubiquitous connectivity among objects is the future of the coming Internet-of-Things (IoT) era. Technologies are competing fiercely to fulfill this goal but none of them can fit into all application scenarios. However, steps are never stopped to take more share of this promising market. Shortly after the adoption of its newest version Bluetooth 5.0, the Bluetooth Special Interest Group (SIG) released another new specification on network topology, Bluetooth Mesh. Combined together, those two bring us a brand-new Bluetooth, opening up new opportunities to both the technology itself and academia. In this article, updated features of it are introduced and presented with use cases that may benefit from those updates. And comparisons on Bluetooth with competitive technologies get conducted accordingly for discussions on pros and cons of the new Bluetooth. Besides commercial applications, we surveyed researches conducted upon the new Bluetooth and discuss how academia can further utilize the new Bluetooth. Through this survey, we show that the new Bluetooth not only consolidates its strengths in original application fields but also brings new opportunities both commercially and academically, making it a strong competitor in the future of providing complete solutions to meet the demands of communications in IoT area.
Event-triggered wireless sensing systems are an important class of wireless sensor network, where the detection of non-deterministic events enables the monitoring and possibly the control of processes in industries such as manufacturing, healthcare and agriculture. The system properties of low latency, energy-efficiency and adaptability make event-triggered wireless sensing systems a key technological enabler for the Industrial Internet of Things. Wireless sensing systems based on periodic multi-hop communication exhibit a fundamental trade-off between latency and energy-efficiency, which is unfavorable for event-triggered application scenarios. In order to address this technological gap, we present BLITZ, the first communication architecture that combines asynchronous and synchronous flooding primitives to facilitate low latency and energy-efficient multi-hop communication of non-deterministic events. BLITZ also incorporates a novel scheme for mitigating erroneous wake-ups, which is shown analytically and experimentally to further reduce energy consumption. We present a prototype implementation of BLITZ and evaluate its performance in an indoor testbed deployment. Experiments show that BLITZ supports a mean latency as low as 108.9ms for an 8-bit event packet and its associated data packet of 32 bytes through a 4-hop network, and a power dissipation of 16uW during periods of inactivity.
Data synchronization is crucial in ubiquitous computing systems, where heterogeneous sensor devices, modalities, and different communication capabilities and protocols are the norm. Further, a common notion of time among devices is required to make sense of their sensing data. Traditional synchronization methods rely on wireless communication between devices to synchronize, potentially incurring computational and power costs. Furthermore, they are unsuitable for synchronizing data streams that have already been collected. We present CRONOS: a post-hoc, data-driven framework for sensor data synchronization for wearable and Internet of Things devices that takes advantage of independent, omni-present motion events in the data streams of two or more sensors. Experimental results on pairwise and multi-sensor synchronization show a drift improvement as high as 98% for total drift and a mean absolute synchronization error of approximately 6 ms for multi-sensor synchronization with sensors sampling at 100Hz.
In this paper, we present a fog computing technique for real-time activity recognition and localization on-board wearable Internet of Things(IoT) devices. Our technique makes joint use of two light-weight analytic methods - Iterative Edge Mining(IEM) and Cooperative Activity Sequence-based Map Matching(CASMM). IEM is a decision-tree classifier that uses acceleration data to estimate the activity state. The sequence of activities generated by IEM is analyzed by the CASMM method for identifying the location. The CASMM method uses cooperation between devices to improve accuracy of classification, and then performs map-matching to identify the location. We evaluate the performance of our approach for activity recognition and localization of animals. The evaluation is performed using real-world acceleration data of cows collected during a pilot study in Dairygold-sponsored farm in Kilworth, Ireland. The analysis shows that our approach can achieve a localization accuracy of upto 99%. In addition, we exploit the location-awareness of devices and present an event-driven communication approach to transmit data from the IoT devices to the cloud. The delay-tolerant communication facilitates context aware sensing and significantly improves energy profile of the devices. Furthermore, an array-based implementation of IEM is discussed and resource assessment is performed to verify its suitability for device-based implementation.
With the remarkable proliferation of smart mobile devices, mobile crowdsensing has emerged as a compelling paradigm to collect and share sensor data from surrounding environment. In many application scenarios, due to unavailable wireless network or expensive data transfer cost, it is desirable to offload crowdsensing data traffic on opportunistic device-to-device (D2D) networks. However, coupling between mobile crowdsensing and D2D networks, it raises new technical challenges caused by intermittent routing and indeterminate settings. Considering the operations of data sensing, relaying, aggregating and uploading simultaneously, in this paper, we study collaborative mobile crowdsensing in opportunistic D2D networks. Towards the concerns of sensing data quality, network performance and incentive budget, Minimum-Delay-Maximum-Coverage (MDMC) problem and Minimum-Overhead-Maximum-Coverage (MOMC) problem are formalized in order to optimally search a complete set of crowdsensing task execution schemes over user, temporal and spatial three dimensions. By exploiting mobility traces of users, we propose an unified graph-based problem representation framework, and transform MDMC and MOMC problems to a connection routing searching problem on weighted directed graphs. Greedy-based recursive optimization approaches are proposed to address the two problems with a divide-and-conquer mode. Empirical evaluation on both real-world and synthetic data sets validates the effectiveness and efficiency of our proposed approaches.
Big data phenomenon has gained much attention in wireless communications field. Addressing big data is a challenging that requires a large computational infrastructure to ensure successful data processing. In such context, data compression helps to reduce the data amount required to represent redundant information while reliably preserving the original content as much as possible. We here consider Compressed Sensing (CS) theory for extracting critical information and representing it with substantially reduced measurements of the original data. However, CS application requires a designing of convenient sparsifying transform. In this work, bi-dimensional 2D correlated signals are considered for compression. The envisaged application is that of data collection in large scale WSN. We show that it is possible to recover the large amount of data from the collection of a reduced sensors readings number. For sparsifying basis search, we propose a new transformation based on Linear Prediction Coding (LPC) which effectively exploits correlation between neighboring data. The steps of data aggregation using CS include sparse compression basis design, then decomposition matrix construction and recovery algorithm application. Simulation results on both synthetic and real WSN data demonstrate the enhanced reconstruction performance of the proposed LPC approach with 2D scenario compared to former conventional transformations.
WSNs have been used for many long-term monitoring applications with the common strip topology that is ubiquitous in real-world deployment, such as pipeline monitoring, water quality monitoring and the Great Wall monitoring. The efficiency of routing strategy has been playing a key role in serving such monitoring applications. In this paper, we present a robust geographic opportunistic routing approach Light Propagation Selection (LIPS) that can provide a short path with low energy consumption, communication overhead and packet loss. To overcome the complication caused by the multi-turning point structure, we propose Virtual Plane Mirror (VPM) algorithm, inspired by the light propagation, which is to map the strip topology into the straight logically. We then select partial neighbors as the candidates to avoid blindly involving all next-hop neighbors and ensure the data transmission along the correct direction. Two implementation problems of VPM, spread angle and the communication range, are thoroughly analyzed based on the percolation theory. By theoretical analysis and extensive simulation we illustrate the validity and higher performance of LIPS in strip WSNs.