Lightweight Dependable Adaptation for Wireless Sensor Networks

Achieving dependable and real-time operation in Wireless Sensor Networks (WSNs) is a hard and open problem. This can be an obstacle for many applications, namely in the automotive and medical domains, particularly if safety-critical control is envisaged. To overcome the communication uncertainties that are intrinsic to wireless and dynamic environments, a generic approach is to constantly adapt to environment conditions. This requires appropriate solutions to characterize such conditions. This paper contributes with a lightweight solution for a dependable characterization of network QoS metrics, which is appropriate to support dependable adaptation in WSNs. The proposed solution offers probabilistic guarantees, building on non-parametric stochastic analysis to achieve fast and effective results. The paper also provides an evaluation of the solution.FCT, CMU-Portuga

Data Assimilation Based on Sequential Monte Carlo Methods for Dynamic Data Driven Simulation

Simulation models are widely used for studying and predicting dynamic behaviors of complex systems. Inaccurate simulation results are often inevitable due to imperfect model and inaccurate inputs. With the advances of sensor technology, it is possible to collect large amount of real time observation data from real systems during simulations. This gives rise to a new paradigm of Dynamic Data Driven Simulation (DDDS) where a simulation system dynamically assimilates real time observation data into a running model to improve simulation results. Data assimilation for DDDS is a challenging task because sophisticated simulation models often have: 1) nonlinear non-Gaussian behavior 2) non-analytical expressions of involved probability density functions 3) high dimensional state space 4) high computation cost. Due to these properties, most existing data assimilation methods fail to effectively support data assimilation for DDDS in one way or another. This work develops algorithms and software to perform data assimilation for dynamic data driven simulation through non-parametric statistic inference based on sequential Monte Carlo (SMC) methods (also called particle filters). A bootstrap particle filter based data assimilation framework is firstly developed, where the proposal distribution is constructed from simulation models and statistical cores of noises. The bootstrap particle filter-based framework is relatively easy to implement. However, it is ineffective when the uncertainty of simulation models is much larger than the observation model (i.e. peaked likelihood) or when rare events happen. To improve the effectiveness of data assimilation, a new data assimilation framework, named as the SenSim framework, is then proposed, which has a more advanced proposal distribution that uses knowledge from both simulation models and sensor readings. Both the bootstrap particle filter-based framework and the SenSim framework are applied and evaluated in two case studies: wildfire spread simulation, and lane-based traffic simulation. Experimental results demonstrate the effectiveness of the proposed data assimilation methods. A software package is also created to encapsulate the different components of SMC methods for supporting data assimilation of general simulation models

A Radio Link Quality Model and Simulation Framework for Improving the Design of Embedded Wireless Systems

Despite the increasing application of embedded wireless systems, developers face numerous challenges during the design phase of the application life cycle. One of the critical challenges is ensuring performance reliability with respect to radio link quality. Specifically, embedded links experience exaggerated link quality variation, which results in undesirable wireless performance characteristics. Unfortunately, the resulting post-deployment behaviors often necessitate network redeployment. Another challenge is recovering from faults that commonly occur in embedded wireless systems, including node failure and state corruption. Self-stabilizing algorithms can provide recovery in the presence of such faults. These algorithms guarantee the eventual satisfaction of a given state legitimacy predicate regardless of the initial state of the network. Their practical behavior is often different from theoretical analyses. Unfortunately, there is little tool support for facilitating the experimental analysis of self-stabilizing systems. We present two contributions to support the design phase of embedded wireless system development. First, we provide two empirical models that predict radio-link quality within specific deployment environments. These models predict link performance as a function of inter-node distance and radio power level. The models are culled from extensive experimentation in open grass field and dense forest environments using all radio power levels and covering up to the maximum distances reachable by the radio. Second, we provide a simulation framework for simulating self-stabilizing algorithms. The framework provides three feature extensions: (i) fault injection to study algorithm behavior under various fault scenarios, (ii) automated detection of non-stabilizing behavior; and (iii) integration of the link quality models described above. Our contributions aim at avoiding problems that could result in the need for network redeployment

Real-time Link Quality Estimation and Holistic Transmission Power Control for Wireless Sensor Networks

Wireless sensor networks (WSNs) are becoming widely adopted across multiple industries to implement sensor and non-critical control applications. These networks of smart sensors and actuators require energy efficient and reliable operation to meet application requirements. Regulatory body restrictions, hardware resource constraints and an increasingly crowded network space makes realising these requirements a significant challenge. Transmission power control (TPC) protocols are poised for wide spread adoption in WSNs to address energy constraints and prolong the lifetime of the networked devices. The complex and dynamic nature of the transmission medium; the processing and memory hardware resource constraints and the low channel throughput makes identifying the optimum transmission power a significant challenge. TPC protocols for WSNs are not well developed and previously published works suffer from a number of common deficiencies such as; having poor tuning agility, not being practical to implement on the resource constrained hardware and not accounting for the energy consumed by packet retransmissions. This has resulted in several WSN standards featuring support for TPC but no formal definition being given for its implementation. Addressing the deficiencies associated with current works is required to increase the adoption of TPC protocols in WSNs. In this thesis a novel holistic TPC protocol with the primary objective of increasing the energy efficiency of communication activities in WSNs is proposed, implemented and evaluated. Firstly, the opportunities for TPC protocols in WSN applications were evaluated through developing a mathematical model that compares transmission power against communication reliability and energy consumption. Applying this model to state-of-the-art (SoA) radio hardware and parameter values from current WSN standards, the maximum energy savings were quantified at up to 80% for links that belong to the connected region and up to 66% for links that belong to the transitional and disconnected regions. Applying the results from this study, previous assumptions that protocols and mechanisms, such as TPC, not being able to achieve significant energy savings at short communications distances are contested. This study showed that the greatest energy savings are achieved at short communication distances and under ideal channel conditions. An empirical characterisation of wireless link quality in typical WSN environments was conducted to identify and quantify the spatial and temporal factors which affect radio and link dynamics. The study found that wireless link quality exhibits complex, unique and dynamic tendencies which cannot be captured by simplistic theoretical models. Link quality must therefore be estimated online, in real-time, using resources internal to the network. An empirical characterisation of raw link quality metrics for evaluating channel quality, packet delivery and channel stability properties of a communication link was conducted. Using the recommendations from this study, a novel holistic TPC protocol (HTPC) which operates on a per-packet basis and features a dynamic algorithm is proposed. The optimal TP is estimated through combining channel quality and packet delivery properties to provide a real-time estimation of the minimum channel gain, and using the channel stability properties to implement an adaptive fade margin. Practical evaluations show that HTPC is adaptive to link quality changes and outperforms current TPC protocols by achieving higher energy efficiency without detrimentally affecting the communication reliability. When subjected to several common temporal variations, links implemented with HTPC consumed 38% less than the current practise of using a fixed maximum TP and between 18-39% less than current SoA TPC protocols. Through offline computations, HTPC was found to closely match the performance of the optimal link performance, with links implemented with HTPC only consuming 7.8% more energy than when the optimal TP is considered. On top of this, real-world implementations of HTPC show that it is practical to implement on the resource constrained hardware as a result of implementing simplistic metric evaluation techniques and requiring minimal numbers of samples. Comparing the performance and characteristics of HTPC against previous works, HTPC addresses the common deficiencies associated with current solutions and therefore presents an incremental improvement on SoA TPC protocols

An approach to understand network challenges of wireless sensor network in real-world environments

The demand for large-scale sensing capabilities and scalable communication networks to monitor and control entities within smart buildings have fuelled the exponential growth in Wireless Sensor Network (WSN). WSN proves to be an attractive enabler because of its accurate sensing, low installation cost and flexibility in sensor placement. While WSN offers numerous benefits, it has yet to realise its full potential due to its susceptibility to network challenges in the environment that it is deployed. Particularly, spatial challenges in the indoor environment are known to degrade WSN communication reliability and have led to poor estimations of link quality. Existing WSN solutions often generalise all link failures and tackle them as a single entity. However, under the persistent influence of spatial challenges, failing to provide precise solutions may cause further link failures and higher energy consumption of battery-powered devices. Therefore, it is crucial to identify the causes of spatial- related link failures in order to improve WSN communication reliability. This thesis investigates WSN link failures under the influence of spatial challenges in real-world indoor environments. Novel and effective strategies are developed to evaluate the WSN communication reliability. By distinguishing between spatial challenges such as a poorly deployed environment and human movements, solutions are devised to reduce link failures and improve the lifespans of energy constraint WSN nodes. In this thesis, WSN test beds using proprietary wireless sensor nodes are developed and deployed in both controlled and uncontrolled office environments. These test beds provide diverse platforms for investigation into WSN link quality. In addition, a new data extraction feature called Network Instrumentation (NI) is developed and implemented onto the communication stacks of wireless sensor nodes to collect ZigBee PRO parameters that are under the influence of environmental dynamics. To understand the relationships between WSN and Wi-Fi devices communications, an investigation on frequency spectrum sharing is conducted between IEEE 802.15.4 and IEEE 802.11 bgn standards. It is discovered that the transmission failure of WSN nodes under persistent Wi-Fi interference is largely due to channel access failure rather than corrupted packets. The findings conclude that both technologies can co- exist as long as there is sufficient frequency spacing between Wi-Fi and WSN communication and adequate operating distance between the WSN nodes, and between the WSN nodes and the Wi-Fi interference source. Adaptive Network-based Fuzzy Inference System (ANFIS) models are developed to predict spatial challenges in an indoor environment. These challenges are namely, “no failure”, “failure due to poorly deployed environment” and “failure due to human movement”. A comparison of models has found that the best-produced model represents the properties of signal strength, channel fluctuations, and communication success rates. It is recognised that the interpretability of ANFIS models have reduced due to the “curse of dimensionality”. Hence, Non-Dominated Sorting Genetic Algorithm (NSGA-II) technique is implemented to reduce the complexity of these ANFIS models. This is followed by a Fuzzy rule sensitivity analysis, where the impacts of Fuzzy rules on model accuracy are found to be dependent on factors such as communication range and controlled or uncontrolled environment. Long-term WSN routing stability is measured, taking into account the adaptability and robustness of routing paths in the real-world environments. It is found that routing stability is subjected to the implemented routing protocol, deployed environment and routing options available. More importantly, the probability of link failures can be as high as 29.9% when a next hop’s usage rate falls less than 10%. This suggests that a less dominant next hop is subjected to more link failures and is short-lived. Overall, this thesis brings together diverse WSN test beds in real-world indoor environments and a new data extraction platform to extract link quality parameters from ZigBee PRO stack for a representative assessment of WSN link quality. This produces realistic perspectives of the interactions between WSN communication reliability and the environmental dynamics, particularly spatial challenges. The outcomes of this work include an in-depth system level understanding of real-world deployed applications and an insightful measure of large-scale WSN communication performance. These findings can be used as building blocks for a reliable and sustainable network architecture built on top of resource–constrained WSN