Low-power consumption and network resiliency are among the vital qualities for having a seamless, quality-oriented wireless communication. Networks with small-world property are known to possess both these favourable qualities. However, wireless networks are not inherently small-world, neither is easy and cost-effective to artificially create networks with this property by using the existing techniques. In other words, the traditional blind rewiring techniques that aimed at enhancing the network with such features, suffer from inefficiency and saturation behaviour. In this study, the authors propose topology-planning methods that efficiently exploit the expensive long-reach transmission facilities to add the small-world property to the network. The authors show that these methods are practical, cost-effective and efficient since they are appropriately tailored based upon the network realities, such as topology and channel fading. The proposed methods are tested for networks with diverse ranges of ‘clustering coefficient’ and ‘diameter’ in order to prove their aptitudes in dealing with real situations. The results illustrate that the incorporation of these techniques altogether decreases the network ‘diameter’ by almost 50% and the ‘average path length’ by 47%. This corresponds to 67% less facilities compared with blind rewiring techniques.

Today, the majority of wagon failures on railroad systems are because of the poor maintenance of ball bearings, which causes emergent stops and delays. The existing stationary detectors, lack in predicting failures which cause troubles in scheduling maintenance. During the fall of 2011, a trial was performed by applying a wireless sensor network (WSN) aboard a train wagon with the objective to demonstrate a proof of concept for monitoring the temperature of ball bearings aboard the train wagon. This trial investigates several key aspects when applying sensor networks such as radio wave propagation, energy scavenging and performance of the WSN aboard the wagon. Two wireless links were used in the WSN. The aboard network communicates at 2.45 GHz, and the external communication is an 868 MHz radio frequency identification radio link. Since the energy in the WSN node is limited, appropriate energy scavenging devices are also presented and evaluated in a lab environment. Effort has been made to overcome these problems. The energy consumption in the network is still a problem; the most promising energy scavenging technique is piezoelectric harvesting by vibrations, which in the experiments scavenged 2.32 mW.

Sensor–cloud computing is envisioned to be one of the key enabling technologies for remote health monitoring. Integration of sensed data into cloud applications in sensor–cloud will help in real-time monitoring of patients over geographically distributed locations. In this study, the authors study the optimal gateway selection problem in sensor–cloud framework for real-time patient monitoring system by using a zero-sum game model. In the proposed model, a gateway acts as the first player, and chooses the strategy based on the available bandwidth, whereas a user request acts as the second player, and follows the strategy chosen by the first player. The authors evaluate the execution time for selecting the optimal gateway through which the sensed data will be fetched to the cloud platform. In addition, the authors show how user requests are serviced by the gateways to access data from cloud platform optimally. The authors also show that by using the proposed approach, the execution time decreases, thereby helping in forming a reliable, efficient and real-time architecture for health monitoring.

The reliable operation of offshore located wind farms is utmost important to ensure consistent power supply to grid. However, this depends on turbine-generator availability. The health monitoring of wind system has proven as one of the most efficient methods to improve the availability of turbine-generator set. This has emerged as one of the many possible applications of wireless sensor networks. This study discusses a novel algorithm for routing protocol of sensor data transmission to the base station. The algorithm is based on three-level hierarchy, spatial distribution of sensor nodes and use of the current energy level of the node for clustering and information routing. This protocol utilises the best of all methods and provides better energy-efficiency and a greater network lifetime compared with the low-energy adaptive clustering hierarchy protocol protocol.

Energy harvesting has been demonstrated to be a promising approach to mitigating energy constraints. Unlike battery-based energy, available system energy significantly varies for energy-harvesting systems. Partial dynamic reconfiguration is adopted as an effective approach for accelerating wireless sensor network (WSN) applications. Although a reconfigurable system can achieve better performance compared with software implementation, reconfiguration potentially requires a large amount of energy and time, particularly for cases where reconfiguration occurs frequently. To address this issue, a novel weather-aware scheduler based on the weather-conditioned moving average (WCMA) prediction algorithm is proposed in this study. To demonstrate the authors approach, a heterogeneous reconfigurable node is also proposed. The implementation of the proposed approach can improve reconfigurable system performance by up to 50% under energy-harvesting conditions. The novelty of this work is 2-fold. First, a prototype is adopted to demonstrate its efficiency for WSN. Second, a novel scheduler is proposed to manage hardware reconfiguration. In the scheduler, WCMA is used to predict future harvested energy.

In cognitive radio, accurate spectrum sensing is essential to optimally use the available spectrum opportunities. On the other hand, energy is a scarce resource especially in cognitive sensor networks. In this study, the authors combine both these conflicting requirements and propose an energy-aware secondary user selection algorithm for cognitive sensor networks. First, an optimisation problem is solved to obtain the minimum required number of cognitive users, whereas satisfying the system requirements. Second, the most eligible cognitive users are identified through a probability-based approach. They study two extreme cases by focusing on either energy or accuracy parameters. By numerical analysis, it is shown that the accuracy benchmark is increased by as much as 39% by only considering the sensing accuracy, and the energy benchmark is reduced by as low as 76% by only considering the remaining level of energy. In addition, they conduct computer simulation and compare the network's lifetime at several sensing accuracy thresholds. It is elaborated that greater sensing accuracy thresholds lead to longer network lifetime. Finally, the effects of several fusion rules on the proposed method are studied through simulation and numerical analyses. It is discussed that the Majority rule has the best performance among the examined rules.

Wireless sensor networks usually obtain the location of an unknown node by measuring the distance between the unknown node and its neighbouring anchors. To enhance both localisation accuracy and localisation success rates, the authors introduce a new neural network-based node localisation scheme. The new scheme is distinct because it can make the trained network model completely relevant to the topology via online training and correlated topology-trained data and therefore attain more efficient application of the neural networks and more accurate inter-node distance estimation. It is also distinct in adopting both received signal strength indication and hop counts to estimate the inter-node distances, to improve the distance estimation accuracy as well as localisation accuracy at no additional cost. Experimental evaluation is conducted to measure the performance of the proposed scheme and other artificial intelligent-based node localisation schemes. The results show that, at reasonable cost, the new scheme constantly produces higher localisation success rates and smaller localisation errors than other schemes.