ACM Transactions on

Sensor Networks (TOSN)

Latest Articles

Radiation Constrained Fair Charging for Wireless Power Transfer

Recently, wireless power transfer technology (WPT) has attracted considerable attention and become a promising technology to prolong the lifetime of... (more)

Known and Unknown Facts of LoRa: Experiences from a Large-scale Measurement Study

Long Range (LoRa) is a Low-power Wide-area Network technology designed for the Internet of Things. In recent years, it has gained significant momentum among industrial and research communities. Patented by Semtech, LoRa makes use of chirp spread spectrum modulation to deliver data with promises of long battery life, far-reaching communication... (more)

LaPS: LiDAR-assisted Placement of Wireless Sensor Networks in Forests

The deployment of a wireless sensor network (WSN) is crucial to its reliability and performance. Yet, node placement is typically determined in-field via effort-demanding trial-and-error procedures, because existing approaches over-simplify the radio environment; this especially holds for forests, the focus of this article, where trees greatly... (more)

Routing-Aware and Malicious Node Detection in a Concealed Data Aggregation for WSNs

Data aggregation in Wireless Sensor Networks (WSNs) can effectively reduce communication overheads and reduce the energy consumption of sensor nodes.... (more)

Resource-efficient and Automated Image-based Indoor Localization

Image-based indoor localization has aroused much interest recently because it requires no infrastructure support. Previous approaches on image-based... (more)

ECT: Exploiting Cross-Technology Transmission for Reducing Packet Delivery Delay in IoT Networks

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same Industrial, Scientific, and Medical band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As... (more)

Participant Incentive Mechanism Toward Quality-Oriented Sensing: Understanding and Application

The ubiquity of ever-more-capable mobile devices, especially smartphones, brings forth participatory sensing to collect and interpret information. It... (more)

A Novel Authenticated Key Agreement Protocol With Dynamic Credential for WSNs

Public key cryptographic primitive (e.g., the famous Diffie-Hellman key agreement, or public key encryption) has recently been used as a standard... (more)

Leveraging Fog Analytics for Context-Aware Sensing in Cooperative Wireless Sensor Networks

In this article, we present a fog computing technique for real-time activity recognition and localization on-board wearable Internet of Things(IoT)... (more)

Low-Cost and Robust Geographic Opportunistic Routing in a Strip Topology Wireless Network

Wireless sensor networks (WSNs) have been used for many long-term monitoring applications with the strip topology that is ubiquitous in the real-world... (more)

BLITZ: Low Latency and Energy-Efficient Communication for Event-Triggered Wireless Sensing Systems

Event-triggered wireless sensing systems are an important class of wireless sensor network, where the detection of non-deterministic events enables the monitoring and control of processes in industries such as manufacturing, healthcare, and agriculture. The system properties of low latency, energy efficiency, and adaptability make event-triggered... (more)


About TOSN

TOSN publishes original research papers (approximately 30-40 printed pages each in ACM Transaction style) and tutorial and survey papers (approximately 30-50 printed pages each). We also accept short technical notes that focus on industrial technologies and practical experience.
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Forthcoming Articles

Power-Positive Networking: Wireless-Charging-Based Networking to Protect Energy Against DoS Attacks

Multicast Scaling of Capacity and Energy Efficiency in Heterogeneous Wireless Sensor Networks

Motivated by the requirement of heterogeneity in IoT, we study the capacity and energy efficiency scaling laws in heterogeneous wireless sensor networks. Assume that the network is composed of n nodes scattered in a square region according to cluster process and ns multicast sessions of size k are scheduled. The network could be classified into two regimes: cluster-dense and cluster-sparse based on the degree of heterogeneity. For each regime we build the multicast spanning tree to forward packet using percolation theory and analyze the corresponding lower bound of scaling laws. The upper bound is obtained using the method of max-flow and min-cut. The simulations in synthetic networks and GreenOrbs sensor network verify that the scaling laws of the packet forwarding scheme is well approximated by the analytical results.

A Survey on Bluetooth 5.0 and Mesh: New Milestones of IoT

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.

Exploiting Concurrency for Opportunistic Forwarding in Duty-cycled IoT Networks

In this paper, we propose COF to fully exploit the potential Concurrency for Op- portunistic Forwarding in duty-cycled IoT networks. COF achieves concurrent transmission by: i) measuring conditional link quality under the interference of on-going transmissions, and then ii) further modeling the benefit of potential concurrency opportunities. According to the expected benefit of concurrency, COF de- cides whether or not to transmit in concurrent way. COF also adopts concurrency flag and signal features to avoid data collision caused by disordered concurrent transmissions and enhance the accuracy of conditional link quality estimation. COF can be easily integrated into the conventional unsynchronized and duty-cycled protocols. We have implemented COF and evaluated its performance on a 40-node testbed. The results show that COF can effectively exploit potential concurrency in opportunistic forwarding and COF outperforms the state of art protocols under diverse traffic load and network density.

CRONOS: A Post-hoc Data Driven Multi-Sensor Synchronization Approach

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.

Collaborative Mobile Crowdsensing in Opportunistic D2D Networks: A Graph-Based Approach

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.

Bi-dimensional Signal Compression based on Linear Prediction Coding: Application to WSN

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.

On Realistic Target Coverage by Autonomous Drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent visual sensing systems. This potential motivated several research efforts to employ drones as standalone surveillance systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain directions; 3) Occlusion by elements in the environment, including other targets. In this paper, we present \textit{Argus}, a system that provides visual coverage of wide and oriented targets, using camera-mounted drones, taking into account the challenges stated above. Argus relies on a geometric model that captures both target shapes and coverage constraints. With drones being the scarcest resource in Argus, we study the problem of minimizing the number of drones required to cover a set of such targets and derive a best-possible approximation algorithm. Building upon that, we present a sampling heuristic that performs favorably, while running up to 100x faster compared to the approximation algorithm. We implement a complete prototype of Argus to demonstrate and evaluate the proposed coverage algorithms within a fully autonomous surveillance system. Finally, we present extensions and discuss open problems related to the studied problem

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