ACM Transactions on

Sensor Networks (TOSN)

Latest Articles

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. A common notion of time among devices is required to make sense of their sensing data. Traditional synchronization methods rely on wireless communication between... (more)

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

Energy is required for networking and computation and is a valuable resource for unplugged systems such as mobile, sensor, and embedded systems. Energy denial-of-service (DoS) attack where a remote attacker exhausts the victim’s battery via networking remains a critical challenge for the device availability. While prior literature proposes... (more)

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 era. Technologies are competing fiercely to fulfill this goal, but none of them can fit into all application scenarios. However, efforts are still made to expand application ranges of certain technologies. Shortly after the adoption of its newest version, Bluetooth... (more)

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

The big data phenomenon has gained much attention in the wireless communications field. Addressing big data is a challenging and time-demanding task... (more)

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... (more)

Exploiting Concurrency for Opportunistic Forwarding in Duty-Cycled IoT Networks

Due to limited energy supply of Internet of Things (Zhao et al. 2018) (IoT) devices, asynchronous duty cycle radio management is widely adopted to... (more)

On Realistic Target Coverage by Autonomous Drones

Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. To achieve the full potential of... (more)

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

Motivated by the requirement of heterogeneity in the Internet of Things, we initiate the joint study of capacity and energy efficiency scaling laws in... (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
Improved Ahead-of-Time Compilation of Stack-Based JVM Bytecode on Resource-Constrained Devices

Many virtual machines exist for sensor nodes with only a few KB RAM and tens to a few hundred KB flash memory. They pack an impressive set of features, but suffer from a slowdown of one to two orders of magnitude compared to optimised native code, reducing throughput and increasing power consumption. Compiling bytecode to native code to improve performance has been studied extensively for larger devices, but the restricted resources on sensor nodes mean most modern techniques cannot be applied. Simply replacing bytecode instructions with predefined sequences of native instructions is known to improve performance, but produces code several times larger than the optimised C equivalent, limiting the size of programmes that can fit onto a device. This paper identifies the major sources of overhead resulting from this basic approach, and presents optimisations to remove most of the remaining performance overhead, and over half the size overhead, reducing them to 69% and 91% respectively. While this increases the size of the VM, the break-even point at which this fixed cost is compensated for is well within the range of memory available on a sensor device, allowing us to both improve performance and load more code on a device.

One-hop Out-of-band Control Planes for Multi-hop Wireless Sensor Networks

Separation of control and data planes (SCDP) is a desirable paradigm for low-power multi-hop wireless sensor networks requiring high network performance and manageability. Existing SCDP networks generally adopt an in-band control plane scheme in that the control-plane messages are delivered by their data-plane networks. The physical coupling of the two planes may lead to undesirable consequences. Recently, multi-radio platforms are increasingly available, making the physical separation of the two planes possible. To advance the network architecture design, we leverage on the long-range communication capability of low-power wide-area network radios to form one-hop out-of-band control planes. We choose LoRaWAN to prototype our out-of-band control plane called LoRaCP. Several characteristics of LoRaWAN such as downlink-uplink asymmetry and primitive ALOHA media access control (MAC) need to be dealt with to achieve high reliability and efficiency. We design a TDMA-based multi-channel MAC featuring an urgent channel and negative acknowledgment. On a testbed of 16 nodes, we demonstrate applying LoRaCP to physically separate the control-plane network of the Collection Tree Protocol from its ZigBee-based data-plane network. Extensive experiments show that LoRaCP increases packet delivery ratio from 65% to 80% in the presence of external interference, while consuming a per-node radio power of 2.97mW only.

BuildSense: Accurate, cost-aware, fault-tolerant monitoring with minimal sensor infrastructure

Buildings can achieve energy-efficiency by using solar passive design, energy-efficient structures and materials, or by optimizing their operational energy use. In each of these areas, efficiency can be improved if the physical properties of the building along with its dynamic behavior can be captured using low-cost embedded sensor devices. This opens up a new challenge of installing and maintaining the sensor devices for different types of buildings. In this article, we propose BuildSense, a sensing framework for fine-grained, long-term monitoring of buildings using a mix of physical and virtual sensors. It not only reduces the deployment and management cost of sensors but can also guarantee accurate and fault-tolerant data coverage for long-term use. We evaluate BuildSense using sensor measurements from two rammed-earth houses that were custom-designed for a challenging hot-arid climate such that almost no artificial heating or cooling is required. We demonstrate that BuildSense can significantly reduce the costs of permanent physical sensors whilst still achieving fit-for-purpose accuracy and stability. Overall, we were able to reduce the cost of a building sensor network by 60\% to 80\% by replacing physical sensors with virtual ones while still maintaining accuracy of $\leq$1.0\textdegree\,C and fault-tolerance of $\geq 2$ predictors per sensor.

From Real to Complex:Enhancing Radio-based Activity Recognition Using Complex-Valued CSI

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information(CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference(RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

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