Jia-Liang Lu

Surface Coverage in Sensor Networks

Coverage is a fundamental problem in wireless sensor networks (WSNs). Conventional studies on this topic focus on 2D ideal plane coverage and 3D full space coverage. The 3D surface of a field of interest (FoI) is complex in many real-world applications. However, existing coverage studies do not produce practical results. In this paper, we propose a new coverage model called surface coverage. In surface coverage, the field of interest is a complex surface in 3D space and sensors can be deployed only on the surface. We show that existing 2D plane coverage is merely a special case of surface coverage. Simulations point out that existing sensor deployment schemes for a 2D plane cannot be directly applied to surface coverage cases. Thus, we target two problems assuming cases of surface coverage to be true. One, under stochastic deployment, what is the expected coverage ratio when a number of sensors are adopted? Two, if sensor deployment can be planned, what is the optimal deployment strategy with guaranteed full coverage with the least number of sensors? We show that the latter problem is NP-complete and propose three approximation algorithms. We further prove that these algorithms have a provable approximation ratio. We also conduct extensive simulations to evaluate the performance of the proposed algorithms.

A Survey on Multipacket Reception for Wireless Random Access Networks

Multipacket reception (MPR) is the capability of simultaneous decoding of more than one packet from multiple concurrent transmissions. Continuous investigations on increasing the reception capability are giving new scientific contributions. In this paper, we provide an overview of MPR-related research work covering (1) the theoretically proved impacts and advantages of using MPR from a channel perspective to network capacity and throughput; (2) the various technologies that enable MPR from transmitter, transreceiver, and receiver perspectives; (3) previous work on protocol improvement to better exploit MPR. Indeed, MPR approaches have been applied in modern wireless mobile systems but the focus of this paper is to discuss MPR in random access wireless networks. Using MPR in such multihop environments calls for new adaptation on protocols, especially a cross-layer approach. To this end, we detail a scheduling method that targets full utilization of MPR capability.

An Anti-Detection Moving Strategy for Mobile Sink

Sink mobility has attracted much research interests in Wireless Sensor Networks (WSNs), because it could provide energy saving and reduce latency during data collection. However, the mobile sink node is still a single point of failure in many WSNs applications, thus needs to be particularly protected against adversaries. We propose in this paper a moving strategy for the mobile sink which prevents tracking or detecting on it by adversaries during its data collection phase around the sensor field. Our moving strategy aims to selecting a trajectory for mobile sink node, which minimizes the total number of message communication from all static sensor nodes to the mobile sink node (including multi-hop relaying) and thereby reducing the possibility of being detected by the adversaries. We also employed a routing protocol on sensor nodes to forward the data to mobile sink node with shortest-path. Four strategies are evaluated in our simulation and the performance results show that our moving strategy we proposed achieves best goal and adapts well to the different deployment patterns.

WiBEST: A hybrid personal indoor positioning system

This paper introduces WiBEST, Wireless Body and Environmental Sensor Tracking platform, for personal indoor positioning. WiBEST is built with portable on-body sensor nodes and assisted sensor nodes deployed in the targeted indoor area. It takes a hybrid approach with pedestrian dead reckoning and radio-based localization and explore the their cooperative efforts. Real-time inertial measurements are combined with RSSI-based information, and then processed with an Extended Kalman Filter to be weighted in the location estimation according to their reliability. WiBEST also incorporates with an adaptive Step Length Algorithm to reduce the deviation of the measurements.The experimentation results show that WiBEST can improve the accuracy of the positioning by 66.3% compared to pure inertial solution. With the popularity of wearable devices with inertial sensors and wireless communication chips, we believe that this approach is very promising for personal indoor positioning services.

Multiple attributes-based data recovery in wireless sensor networks

In wireless sensor networks (WSNs), since many basic scientific works heavily rely on the complete sensory data, data recovery is an indispensable operation against the data loss. Several works have studied the missing value problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a multiple attributes-based recovery algorithm which can provide high accuracy. Firstly, based on two real datasets, the Intel Indoor project and the GreenOrbs project, we reveal that such correlations are strong, e.g., the change of temperature and light illumination usually has strong correlation. Secondly, motivated by this observation, we develop a Multi-Attribute-assistant Compressive-Sensing-based (MACS) algorithm to optimize the recovery accuracy. Finally, real trace-driven simulation is performed. The results show that MACS outperforms the existing solutions. Typically, MACS can recover all data with less than 5% error when the loss rate is less than 60%. Even when losing 85% data, all missing data can be estimated by MACS with less than 10% error.

Behavior-aware probabilistic routing for wireless body area sensor networks

Recent advances in wireless communication and electronic manufacture have enabled a variety of sensors to be used for Wireless Body Area Networks (WBANs), which can provide real-time body monitoring and feedback for enabling patient diagnostics procedure, rehabilitation, sports training and interactive performance. However, existing single-hop wireless communication scheme faces several major challenges: rapid growth of channel conflicts as more sensors added, impermeability of human body to radio waves and highly dynamic network topology due to human movements. In this paper, a prototype of multi-hop WBAN has been built to quantify the channel conflict and to characterise the network connectivity during human motions. A probability based routing protocol fusing inertial sensor data and history link quality is then developed, which aims at capturing the high spatio-temporal change of network topology on the selection of a reliable relay node in WBAN routing. The performance of the protocol is experimentally evaluated on our prototype system. Compared with a number of existing routings, the proposed scheme is more splendid in terms of average delivery ratio, number of hops and end-to-end delay.

Heterogeneous Task Allocation in Participatory Sensing

The proliferation of smartphones has enabled a novel paradigm, participatory sensing, which leverages the smartphones to collect and share data about their surrounding environment. Since the sensing tasks are location-dependent and have time features, it is crucial and challenging to find a proper allocation of sensing tasks to ensure the timeliness of tasks and the quality of sensing data. In this paper, we investigate the heterogeneous sensing task allocation problem aiming at minimizing the total penalty caused by the tardiness of tasks. We prove this problem is NP-hard and propose two hybrid algorithms which combine a heuristic algorithm and two meta-heuristic algorithms respectively. The extensive simulation results show that the proposed hybrid algorithms outperform the meta-heuristic algorithms.

Efficient and reliable MAC-layer broadcast for IEEE 802.15.4 Wireless Sensor Networks

IEEE 802.15.4 represents a widely used MAC-layer standard for Wireless Sensor Networks. In multihop topologies, the protocol exploits a cluster-tree and organizes the transmissions by alternating sleeping and active periods in a superframe delimited by beacons. In this paper, we propose a new Contention Broadcast Only Period to limit beacon collisions and to reduce bandwidth wastage due to variable beacon durations. We adopt a CSMA-approach during the Contention Broadcast Only Period to efficiently deliver both beacon and broadcast packets. We also propose to use broadcast sequence numbers for a reliable MAC-layer broadcast delivery, for both cluster-tree and radio neighbors. Simulations with realistic conditions prove the relevance of this approach. We increase energy savings by reducing idle listening, and improve the MAC-layer broadcast reliability for both radio and cluster-tree delivery.

JSSDR: Joint-Sparse Sensory Data Recovery in wireless sensor networks

Data loss is ubiquitous in wireless sensor networks (WSNs) mainly due to the unreliable wireless transmission, which results in incomplete sensory data sets. However, the completeness of a data set directly determines its availability and usefulness. Thus, sensory data recovery is an indispensable operation against the data loss problem. However, existing solutions cannot achieve satisfactory accuracy due to special loss patterns and high loss rates in WSNs. In this work, we propose a novel sensory data recovery algorithm which exploits the spatial and temporal joint-sparse feature. Firstly, by mining two real datasets, namely the Intel Indoor project and the GreenOrbs project, we find that: (1) for one attribute, sensory readings at nearby nodes exhibit inter-node correlation; (2) for two attributes, sensory readings at the same node exhibit inter-attribute correlation; (3) these inter-node and inter-attribute correlations can be modeled as the spatial and temporal joint-sparse features, respectively. Secondly, motivated by these observations, we propose two Joint-Sparse Sensory Data Recovery (JSSDR) algorithms to promote the recovery accuracy. Finally, real data-based simulations show that JSSDR outperforms existing solutions. Typically, when the loss rate is less than 65%, JSSDR can estimate missing values with less than 10% error. And when the loss rate reaches as high as 80%, the missing values can be estimated by JSSDR with less than 20% error.

Multi-attribute compressive data gathering

The data gathering is a fundamental operation in wireless sensor networks. Among approaches of the data gathering, the compressive data gathering (CDG) is an effective solution, which exploits the spatiotemporal correlation of raw sensory data. However, in the multi-attribute scenario, the performance of CDG decreases in every attributes capacity because more measurements are on demand. In this paper, under the general framework of CDG, we propose a multi-attribute compressive data gathering protocol, taking into account the observed interattribute correlation in the multi-attribute scenario. Firstly, we find that 1) the rapid growth of the demand on measurements may decline the network capacity, 2) according to the compressive sensing theory, correlations among attributes can be utilized to reduce the demand on measurements without the loss of accuracy, and 3) such correlations can be found on real data sets. Secondly, motivated by these observations, we propose our approach to decline measurements. Finally, the real-trace simulation shows that our approach outperforms the original CDG under multiattribute scenario. Compared to the CDG, our approach can save 16% demand on measurements.