Image-based indoor localization has aroused much interest recently, because it requires no infrastructure support. Previous approaches on image-based localization, due to their computation and storage requirements, often process queries at server. This does not scale well, incurs round-trip delay, and requires constant network connectivity. Many also require users to manually confirm the shortlisted matched landmarks, which is inconvenient, slow, and prone to selection error. To overcome these limitations, we propose HAIL a highly automated (in terms of image selection or confirmation) image-based localization algorithm distributed in mobile devices. HAIL achieves resource efficiency (in terms of storage and processing) by keeping only those distinguishing visual features for each landmark, and employing a data structure to search for the features efficiently. It further utilizes the motion sensors and map constraint to enhance the localization accuracy without user operation. We have implemented HAIL on Android platform and conducted extensive experiments in a food plaza and a premium shopping mall. Our results show that it achieves much higher localization accuracy (reducing the localization error by more than 20%) and computation efficiency (by more than 40% in time) as compared with the state-of-the-art approaches.
LoRa is one of the LPWAN technologies designed for IoT has gained significant momentum amongst both industrial and research communities. Patented by Semtech, LoRa makes use of chirp spread spectrum modulation to deliver data. LoRa promises long battery life, far-reaching communication distances, and a high node density at the cost of data rate. In this paper we conduct a series of experiments to verify the claims made by Semtech on LoRa technology. Our results show that LoRa is capable of communicating over 10 km under line-of-sight environment however, under non-line-of-sight environments, LoRa performance is severely affected by obstructions such as buildings and vegetations. Moreover, the promised of prolonged battery life requires extreme tuning of parameters. Lastly, a LoRa gateway supports up to 6,000 nodes with PRR requirement of >70%. This study also explores the relationship between LoRa transmission parameters and proposes an algorithm to determine optimal settings in terms of coverage and power consumption under non-line-of-sight environment. It further investigates the impact of LoRaWAN on energy consumption and network capacity along with implementation of LoRa medium access mechanism and possible gains brought forth by implementing such a mechanism.
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.
Public key cryptographic primitive (such as the famous Diffie-Hellman key agreement, or public key encryption) has recently been used as a standard building block in authenticated key agreement (AKA) constructions for wireless sensor networks (WSNs) to provide perfect forward secrecy (PFS), where some expensive cryptographic operation, i.e. exponentiation calculation, is involved. However, realizing such complex computation on resource constrained wireless sensors is inefficient, and even impossible for certain device. In this work, we introduce a new AKA scheme with PFS for WSNs without using any public key cryptographic primitive. In order to achieve PFS, we rely on a new a dynamic one-time authentication credential which is regularly updated in each session. In particular, each authentication credential is wisely associated with at most one session key that enables us to fulfill the security goal of PFS. Furthermore, our scheme can provide impersonation attack detection function which could allow principles to identify whether they have been previously impersonated by some attacker. We highlight that our scheme can be very efficiently implemented on sensors, since only hash function and XOR operation are required.
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.