An adaptive IEEE 802.15.4 MAC layer transmission scheme for applications with heterogeneous traffic

IEEE 802.15.4 networks has the features of low data rate and low power consumption. It is a strong candidate technique for wireless sensor networks and can find many applications to smart grid. However, due to the low network and energy capacities it is critical to maximize the bandwidth and energy efficiencies of 802.15.4 networks. In this paper we propose an adaptive data transmission scheme with CSMA/CA access control, for applications which may have heavy traffic loads such as smart grids. The adaptive access control is simple to implement. Its compatibility with legacy 802.15.4 devices can be maintained. Simulation results demonstrate the effectiveness of the proposed scheme with largely improved bandwidth and power efficiency. © 2013 International Information Institute.

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences.

Adaptive edge analytics for distributed networked control of water systems

Over the last decade, there has been a trend where water utility companies aim to make water distribution networks more intelligent in order to improve their quality of service, reduce water waste, minimize maintenance costs etc., by incorporating IoT technologies. Current state of the art solutions use expensive power hungry deployments to monitor and transmit water network states periodically in order to detect anomalous behaviors such as water leakage and bursts. However, more than 97% of water network assets are remote away from power and are often in geographically remote underpopulated areas, facts that make current approaches unsuitable for next generation more dynamic adaptive water networks. Battery-driven wireless sensor/actuator based solutions are theoretically the perfect choice to support next generation water distribution. In this paper, we present an end-to-end water leak localization system, which exploits edge processing and enables the use of battery-driven sensor nodes. Our system combines a lightweight edge anomaly detection algorithm based on compression rates and an efficient localization algorithm based on graph theory. The edge anomaly detection and localization elements of the systems produce a timely and accurate localization result and reduce the communication by 99% compared to the traditional periodic communication. We evaluated our schemes by deploying non-intrusive sensors measuring vibrational data on a real-world water test rig that have had controlled leakage and burst scenarios implemented.

Low-cost interrogation of optical fibre Bragg grating sensors

Through the application of novel signal processing techniques we are able to measure physical measurands with both high accuracy and low noise susceptibility. The first interrogation scheme is based upon a CCD spectrometer. We compare different algorithms for resolving the Bragg wavelength from a low resolution discrete representation of the reflected spectrum, and present optimal processing methods for providing a high integrity measurement from the reflection image. Our second sensing scheme uses a novel network of sensors to measure the distributive strain response of a mechanical system. Using neural network processing methods we demonstrate the measurement capabilities of a scalable low-cost fibre Bragg grating sensor network. This network has been shown to be comparable with the performance of existing fibre Bragg grating sensing techniques, at a greatly reduced implementation cost.

Recent advances in fibre grating devices and their sensing applications

Over the last twenty years, we have been continuously seeing R&D efforts and activities in developing optical fibre grating devices and technologies and exploring their applications for telecommunications, optical signal processing and smart sensing, and recently for medical care and biophotonics. In addition, we have also witnessed successful commercialisation of these R&Ds, especially in the area of fibre Bragg grating (FBG) based distributed sensor network systems and technologies for engineering structure monitoring in industrial sectors such as oil, energy and civil engineering. Despite countless published reports and papers and commercial realisation, we are still seeing significant and novel research activities in this area. This invited paper will give an overview on recent advances in fibre grating devices and their sensing applications with a focus on novel fibre gratings and their functions and grating structures in speciality fibres. The most recent developments in (i) femtosecond inscription for microfluidic/grating devices, (2) tilted grating based novel polarisation devices and (3) dual-peak long-period grating based DNA hybridisation sensors will be discussed.

How the web of things challenges requirements engineering

As a subset of the Internet of Things (IoT), the Web of Things (WoT) shares many characteristics with wireless sensor and actuator networks (WSANs) and ubiquitous computing systems (Ubicomp). Yet to a far greater degree than the IoT, WSANs or Ubicomp, the WoT will integrate physical and information objects, necessitating a means to model and reason about a range of context types that have hitherto received little or no attention from the RE community. RE practice is only now developing the means to support WSANs and Ubicomp system development, including faltering first steps in the representation of context. We argue that these techniques will need to be developed further, with a particular focus on rich context types, if RE is to support WoT application development. © 2012 Springer-Verlag.

Recent advances in fibre grating devices and their sensing applications

Over the last twenty years, we have been continuously seeing R&D efforts and activities in developing optical fibre grating devices and technologies and exploring their applications for telecommunications, optical signal processing and smart sensing, and recently for medical care and biophotonics. In addition, we have also witnessed successful commercialisation of these R&Ds, especially in the area of fibre Bragg grating (FBG) based distributed sensor network systems and technologies for engineering structure monitoring in industrial sectors such as oil, energy and civil engineering. Despite countless published reports and papers and commercial realisation, we are still seeing significant and novel research activities in this area. This invited paper will give an overview on recent advances in fibre grating devices and their sensing applications with a focus on novel fibre gratings and their functions and grating structures in speciality fibres. The most recent developments in (i) femtosecond inscription for microfluidic/grating devices, (2) tilted grating based novel polarisation devices and (3) dual-peak long-period grating based DNA hybridisation sensors will be discussed.

The application of a long period grating array for respiratory function monitoring

In this paper, multiplexed sensor network capable of monitoring the shape changes of the torso for respiratory function monitoring is developed. As a demonstration, LPGs written into refractive index insensitive, progressive three layered fibre are embedded into supporting material is then placed on a resuscitation training manikin simulating respiration. A derivative spectroscopy interrogation technique is implemented and the bend sensitivity of the LPGs is used to reconstruct the shape of the manikin's torso. © 2003 IEEE.

Message from the UCAWSN 2014 program Chairs

Published in the Front Matter

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

A scatterometer neural network sensor model with input noise

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

The throughput order of multicast traffics with physical-layer network coding in random wireless ad hoc networks

This paper attempts to address the effectiveness of physical-layer network coding (PNC) on the throughput improvement for multi-hop multicast in random wireless ad hoc networks (WAHNs). We prove that the per session throughput order with PNC is tightly bounded as T((nvmR (n))-1) if m = O(R-2 (n)), where n is the total number of nodes, R(n) is the communication range, and m is the number of destinations for each multicast session. We also show that per-session throughput order with PNC is tight bounded as T(n-1), when m = O(R-2(n)). The results of this paper imply that PNC cannot improve the throughput order of multicast in random WAHNs, which is different from the intuition that PNC may improve the throughput order as it allows simultaneous signal access and combination.

A network coding based interference cancelation scheme for wireless ad hoc networks

The performance of wireless networks is limited by multiple access interference (MAI) in the traditional communication approach where the interfered signals of the concurrent transmissions are treated as noise. In this paper, we treat the interfered signals from a new perspective on the basis of additive electromagnetic (EM) waves and propose a network coding based interference cancelation (NCIC) scheme. In the proposed scheme, adjacent nodes can transmit simultaneously with careful scheduling; therefore, network performance will not be limited by the MAI. Additionally we design a space segmentation method for general wireless ad hoc networks, which organizes network into clusters with regular shapes (e.g., square and hexagon) to reduce the number of relay nodes. The segmentation methodworks with the scheduling scheme and can help achieve better scalability and reduced complexity. We derive accurate analytic models for the probability of connectivity between two adjacent cluster heads which is important for successful information relay. We proved that with the proposed NCIC scheme, the transmission efficiency can be improved by at least 50% for general wireless networks as compared to the traditional interference avoidance schemes. Numeric results also show the space segmentation is feasible and effective. Finally we propose and discuss a method to implement the NCIC scheme in a practical orthogonal frequency division multiplexing (OFDM) communications networks. Copyright © 2009 John Wiley & Sons, Ltd.

On the capacity improvement of multicast throughput in wireless ad hoc networks with physical-layer network coding

This paper attempts to address the effectiveness of physical-layer network coding (PNC) on the capacity improvement for multi-hop multicast in random wireless ad hoc networks (WAHNs). While it can be shown that there is a capacity gain by PNC, we can prove that the per session throughput capacity with PNC is ? (nR(n))), where n is the total number of nodes, R(n) is the communication range, and each multicast session consists of a constant number of sinks. The result implies that PNC cannot improve the capacity order of multicast in random WAHNs, which is different from the intuition that PNC may improve the capacity order as it allows simultaneous signal reception and combination. Copyright © 2010 ACM.

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time-varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm. © 2013 John Wiley & Sons, Ltd.