Practical and Robust Power Management for Wireless Sensor Networks

Wireless Sensor Networks: WSNs) consist of tens or hundreds of small, inexpensive computers equipped with sensors and wireless communication capabilities. Because WSNs can be deployed without fixed infrastructure, they promise to enable sensing applications in environments where installing such infrastructure is not feasible. However, the lack of fixed infrastructure also presents a key challenge for application developers: sensor nodes must often operate for months or years at a time from fixed or limited energy sources. The focus of this dissertation is on reusable power management techniques designed to facilitate sensor network developers in achieving their systems\u27 required lifetimes. Broadly speaking, power management techniques fall into two categories. Many power management protocols developed within the WSN community target specific hardware subsystems in isolation, such as sensor or radio hardware. The first part of this dissertation describes the Adaptive and Robust Topology control protocol: ART), a representative hardware-specific technique for conserving energy used by packet transmissions. In addition to these single-subsystem approaches, many applications can benefit greatly from holistic power management techniques that jointly consider the sensing, computation, and communication costs of potential application configurations. The second part of this dissertation extends this holistic power management approach to two families of structural health monitoring applications. By applying a partially-decentralized architecture, the cost of collecting vibration data for analysis at a centralized base station is greatly reduced. Finally, the last part of this dissertation discusses work toward a system for clinical early warning and intervention. The feasibility of this approach is demonstrated through preliminary study of an early warning component based on historical clinical data. An ongoing clinical trial of a real-time monitoring component also provides important guidelines for future clinical deployments based on WSNs

A Machine Learning Enhanced Scheme for Intelligent Network Management

The versatile networking services bring about huge influence on daily living styles while the amount and diversity of services cause high complexity of network systems. The network scale and complexity grow with the increasing infrastructure apparatuses, networking function, networking slices, and underlying architecture evolution. The conventional way is manual administration to maintain the large and complex platform, which makes effective and insightful management troublesome. A feasible and promising scheme is to extract insightful information from largely produced network data. The goal of this thesis is to use learning-based algorithms inspired by machine learning communities to discover valuable knowledge from substantial network data, which directly promotes intelligent management and maintenance. In the thesis, the management and maintenance focus on two schemes: network anomalies detection and root causes localization; critical traffic resource control and optimization. Firstly, the abundant network data wrap up informative messages but its heterogeneity and perplexity make diagnosis challenging. For unstructured logs, abstract and formatted log templates are extracted to regulate log records. An in-depth analysis framework based on heterogeneous data is proposed in order to detect the occurrence of faults and anomalies. It employs representation learning methods to map unstructured data into numerical features, and fuses the extracted feature for network anomaly and fault detection. The representation learning makes use of word2vec-based embedding technologies for semantic expression. Next, the fault and anomaly detection solely unveils the occurrence of events while failing to figure out the root causes for useful administration so that the fault localization opens a gate to narrow down the source of systematic anomalies. The extracted features are formed as the anomaly degree coupled with an importance ranking method to highlight the locations of anomalies in network systems. Two types of ranking modes are instantiated by PageRank and operation errors for jointly highlighting latent issue of locations. Besides the fault and anomaly detection, network traffic engineering deals with network communication and computation resource to optimize data traffic transferring efficiency. Especially when network traffic are constrained with communication conditions, a pro-active path planning scheme is helpful for efficient traffic controlling actions. Then a learning-based traffic planning algorithm is proposed based on sequence-to-sequence model to discover hidden reasonable paths from abundant traffic history data over the Software Defined Network architecture. Finally, traffic engineering merely based on empirical data is likely to result in stale and sub-optimal solutions, even ending up with worse situations. A resilient mechanism is required to adapt network flows based on context into a dynamic environment. Thus, a reinforcement learning-based scheme is put forward for dynamic data forwarding considering network resource status, which explicitly presents a promising performance improvement. In the end, the proposed anomaly processing framework strengthens the analysis and diagnosis for network system administrators through synthesized fault detection and root cause localization. The learning-based traffic engineering stimulates networking flow management via experienced data and further shows a promising direction of flexible traffic adjustment for ever-changing environments

Improving RSSI based distance estimation for wireless sensor networks

In modern everyday life we see gradually increasing number of wireless sensor devices. In some cases it is necessary to know the accurate location of the devices. Most of the usual techniques developed to get this information require a lot of resources (power, bandwidth, computation, extra hardware) which small embedded devices cannot afford. Therefore techniques, using small resources without the need for extra hardware, need to be developed. Wireless sensor networks are often used inside buildings. In such environment satellite positioning is not available. As a consequence, the location computation must be done in network-based manner. In this thesis a received signal strength indicator (RSSI) based distance estimation technique for 802.15.4 network based on CC2431 radio is discussed. In this approach we try to differentiate between good and erroneous measurements by imposing limits based on standard deviation of RSSI and the number of lost packets. These limits are included as a part of the model parameter estimation process. These limits are optimized in order to improve the resulting distance estimates with minimum loss of connectivity information. We experimentally evaluated the merits of the proposed method and found it to be useful under certain circumstances.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Integrated Satellite-terrestrial networks for IoT: LoRaWAN as a Flying Gateway

When the Internet of Things (IoT) was introduced, it causes an immense change in human life. Recently, different IoT emerging use cases, which will involve an even higher number of connected devices aimed at collecting and sending data with different purposes and over different application scenarios, such as smart city, smart factory, and smart agriculture. In some cases, the terrestrial infrastructure is not enough to guarantee the typical performance indicators due to its design and intrinsic limitations. Coverage is an example, where the terrestrial infrastructure is not able to cover certain areas such as remote and rural areas. Flying technologies, such as communication satellites and Unmanned Aerial Vehicles (UAVs), can contribute to overcome the limitations of the terrestrial infrastructure, offering wider coverage, higher resilience and availability, and improving user\u2019s Quality of Experience (QoE). IoT can benefit from the UAVs and satellite integration in many ways, also beyond the coverage extension and the increase of the available bandwidth that these objects can offer. This thesis proposes the integration of both IoT and UAVs to guarantee the increased coverage in hard to reach and out of coverage areas. Its core focus addresses the development of the IoT flying gateway and data mule and testing both approaches to show their feasibility. The first approach for the integration of IoT and UAV results in the implementing of LoRa flying gateway with the aim of increasing the IoT communication protocols\u2019 coverage area to reach remote and rural areas. This flying gateway examines the feasibility for extending the coverage in a remote area and transmitting the data to the IoT cloud in real-time. Moreover, it considers the presence of a satellite between the gateway and the final destination for areas with no Internet connectivity and communication means such as WiFi, Ethernet, 4G, or LTE. The experimental results have shown that deploying a LoRa gateway on board a flying drone is an ideal option for the extension of the IoT network coverage in rural and remote areas. The second approach for the integration of the aforementioned technologies is the deployment of IoT data mule concept for LoRa networks. The difference here is the storage of the data on board of the gateway and not transmitting the data to the IoT cloud in real time. The aim of this approach is to receive the data from the LoRa sensors installed in a remote area, store them in the gateway up until this flying gateway is connected to the Internet. The experimental results have shown the feasibility of our flying data mule in terms of signal quality, data delivery, power consumption and gateway status. The third approach considers the security aspect in LoRa networks. The possible physical attacks that can be performed on any LoRa device can be performed once its location is revealed. Position estimation was carried out using one of the LoRa signal features: RSSI. The values of RSSI are fed to the Trilateration localization algorithm to estimate the device\u2019s position. Different outdoor tests were done with and without the drone, and the results have shown that RSSI is a low cost option for position estimation that can result in a slight error due to different environmental conditions that affect the signal quality. In conclusion, by adopting both IoT technology and UAV, this thesis advances the development of flying LoRa gateway and LoRa data mule for the aim of increasing the coverage of LoRa networks to reach rural and remote areas. Moreover, this research could be considered as the first step towards the development of high quality and performance LoRa flying gateway to be tested and used in massive LoRa IoT networks in rural and remote areas

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig