In this study, real-time design and implementation of an energy-efficient cluster-based protocol for wireless sensor networks (WSNs) are presented. The formation of suitable clusters is of prime importance to balance energy usage by sensor nodes within each cluster of the cluster-based WSNs. This leads to energy savings for the sensor nodes resulting in longer network lifetime. To obtain this objective, the fuzzy C-means (FCM) clustering algorithm is incorporated in the protocol. The protocol is realised on a hardware test-bed with the support of the embedded operating system, TinyOS. Experimental results obtained from a scale-down laboratory based test-bed with up to 50 sensor nodes are provided to illustrate the efficacy of the WSNs using the proposed protocol and compared with the well-known cluster-based protocols such as Low Energy Adaptive Clustering Hierarchy. It has been shown that the FCM protocol is able to achieve better organisation of the network and thus can extend its lifetime under varying operating conditions and with different number of nodes.

In hierarchical sensor networks, key sharing at the intra-cluster tier (i.e. how to establish pairwise keys between a sensor and its cluster head) is more challenging than that at inter-cluster tier. Despite some key management protocols have been proposed, there still lacks both secure and efficient one. In this study, the authors proposed an intra-cluster key sharing to overcome the security problems nowadays. The analysis proved that this proposed scheme has the following advantages by comparing with other schemes: (i) it is more efficient in saving the storage overheads and communication overheads; (ii) it improves the resilience against node capture attacks during and after initialisation; and (iii) it guarantees 100% connectivity at intra-cluster communication tier.

Owing to its cable-free deployment, wireless sensor networks (WSNs) have drawn great attention as a new technique for industrial data acquisition. However, the harsh environment of the gas turbine engine provides a number of challenges to deployment of wireless sensors. A definitive study of the impact of harsh environments on WSNs is currently lacking, which represents an obstacle to WSN's deployment in safety-critical industrial instrumentation and automation. In this study, the authors report the test results of applying WSNs to data acquisition in gas turbine engine testing and the development of a realistic software simulator with the purpose of de-risking the wireless data transmission technology in a project called WIDAGATE (wireless data acquisition in gas turbine engine testing). This study provides an overview of the simulation platform developed and investigates how small-scale tests of a WSN deployed on a real engine were used to validate and improve the simulator platform. This work proposes realistic modelling of the physical layer (radio channel) when subject to interference in harsh industry environment during aero-engine testing. Based on the validated, realistic physical layer model, different medium access control protocols are simulated to demonstrate how this improved simulator can be used to select an appropriate protocol.

The authors address an important aspect of spectrum sensing that has been often overlooked in the cognitive radio (CR) research. Although CR is supposed to be aware of its surrounding, most existing articles do not consider the characteristics of secondary users in the optimisation of sensing period. In this study, based on a continuous-time Markov chain model for cognitive sensor networks and energy detection method, the authors propose an application-specific spectrum sensing method that obtains the optimal sensing period according to the characteristics of both ‘primary and secondary’ users (hybrid scheme). The authors define and analytically derive two parameters, the interference ratio and the undetected opportunity ratio, and analytically find the optimum sensing period. Numerical and simulation results indicate that our proposed method is able to provide an optimal sensing period, that is customised for different cognitive networks. The proposed method significantly increases the system throughput by up to 11% and reduces the network's power consumption by as low as 33%. Finally, the trade-off between the throughput maximisation and power consumption minimisation is discussed.

Quality-of-service-guaranteed video communication in wireless video sensor networks (WVSNs) is extremely challenging because of the unique constraints of WVSN (e.g. limited resources of sensors and high error rates in wireless sensor networks) and the characteristics of video traffic (e.g. huge data rate and tight delay bounds). Distributed video coding (DVC) has emerged as a new video coding paradigm for video applications with resource-limited encoders. However, current DVC architectures still have several technical limitations that prevent their widespread use in real-world applications. A low-complexity distributed multiple description coding method that combines the principles of multiple description coding and DVC has been proposed to further improve the error resilience of DVC, while maintaining low encoder complexity and good rate-distortion performance in WVSN. Simulation results show that the proposed method is robust against transmission errors, while maintaining low encoder complexity and low system latency for resource-limited applications in WVSNs.

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Walker is a widely used mobility aid to improve users’ stability and ambulatory ability. Its dynamometer instrumentation is necessary for quantitative study of basic biomechanics and functional requirements for effective use. How to extract the kinetic demands exactly and without any disturbance to normal gait remains a bottleneck for walker dynamometer design. Based on the force measurement of handle reaction vectors (HRVs) applied to the walker, this study developed a novel strain gauge-based wireless walker dynamometer system integrated with a static calibration algorithm based on ant colony system (ACS). Compared with the traditional measurement of HRV, the proposed method enhances security and flexibility of walker use by using one wireless data transmission system connecting 12 strain-gauge bridges mounted on the walker frame with the computer. To improve high-dimensional calibration performance, an ACS algorithm was employed to optimise the sensitivity weight matrix during calibration. To evaluate force measurement reliability, system performance with ACS algorithm was testified and its mean non-linearity, mean crosstalk and maximal force measurement accuracy error were found to be 6.88, 6.10 and 7.46%, respectively, which were much better than those of traditional linear calibration methods. This implemented walker dynamometer system may prove to be a reliable tool for measurement of hand loads and description of kinetic analysis of basic walker-assisted gait.

A fast tree-search algorithm for joint leak detection and localisation using surface-borne ultrasonic acoustic signals is developed through a wireless sensor network. Owing to environmental noise and multipath fading of ultrasonic signals, false sensor observations are frequent in the observation data. The problem is modelled as a Bayesian inference model and the maximum a posteriori solution is approximated through a tree-search structure. The algorithm initially divides the area into large cells and approximates the observation likelihood function over these large cells. In a tree structure, a large cell with high likelihood is divided into smaller cells and the tree is expanded until the required estimation precision is obtained. Simulation and experimental results reveal advantages of the proposed technique in terms of estimation error and convergence speed in comparison with other conventional Bayesian techniques such as particle filtering.