Multifaceted Optimization of Energy Efficiency for Stationary WSN Applications

Stationary Wireless Sensor Networks (S-WSNs) consist of battery-powered and resource-constrained sensor nodes distributed at fixed locations to cooperatively monitor the environment or an object and provide persistent data acquisition. These systems are being practiced in many applications, ranging from disaster warning systems for instant event detection to structural health monitoring for effective maintenance. Despite the diversity of S-WSN applications, one common requirement is to achieve a long lifespan for a higher value-to-cost ratio. However, the variety of WSN deployment environments and use cases imply that there is no silver bullet to solve the energy issue completely. This thesis is a summary of six publications. Our  contributions include four energy optimization techniques on three layers for S-WSN applications. From the bottom up, we designed an ultra-low power smart trigger to integrate environment perceptibility into the hardware. On the network layer, we propose a reliable clustering protocol and a cluster-based data aggregation scheme. This scheme offers topology optimization together with in-network data processing. On the application layer, we extend an industrial standard protocol XMPP to incorporate WSN characteristics for unified information dissemination. Our protocol extensions facilitate WSN application development by adopting IMPS on the Internet. In addition, we conducted a performance analysis of one lightweight security protocol for WSNs called HIP Diet Exchange, which is being standardized by IETF. We suggested a few improvements and potential applications for HIP DEX. In the process of improving energy efficiency, we explore modular and generic design for better system integration and scalability. Our hardware invention can extend features by adding new transducers onboard. The clustering protocol and data aggregation scheme provides a general self-adaptive method to increase information throughput per energy cost while tolerating network dynamics. The unified XMPP extensions aim to support seamless information flow for the Web of Things. The results presented in this thesis demonstrate the importance of multifaceted optimization strategy in WSN development. An optimal WSN system should comprehend multiple factors to boost energy efficiency in a holistic approach

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Smart Sensor Data Acquisition in trains

Whether for work or leisure, we see a large number of people traveling by train every day. In order to ensure the comfort and safety of passengers, it must be checked whether the composition is working normally. For this purpose, a constant monitoring of a train must be done, followed by a diagnosis of the com-position, prediction of failures and production of alarms in the event of any anomaly. To perform monitoring on a train, it is necessary to collect data from sensors distributed along its carriages and send them to a software system that performs the diagnosis of the composition in a fast and efficient way. The description of the activities necessary for monitoring of a train imme-diately refers to topics such as distributed systems, since the intended system will have to integrate several sensors distributed along the train, or Smart Systems, since each sensor must have the capacity to not only acquire data, but also trans-mit it, preferably, wirelessly. However, there are some obstacles to the implementation of such a system. Firstly, the existence of sources of distortions and noise in the medium interferes both in the acquisition and transmission of data and secondly the fact that the sensors distributed along the train are not prepared to be connected directly to a software system. This dissertation seeks to find a solution for the problems described by im-plementing a data acquisition system that is distributed and takes advantage of the current technologies of low-cost sensor nodes as well as web technologies for sensor networks

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3D Indoor Monitoring

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt and zoom (ptz), the sensing range of a video sensor can be further regarded as a fan-shape in 2d and pyramid-shape in 3d. Such uniqueness attributed to wireless video sensors and the challenges associated with deployment restrictions of indoor monitoring make the traditional sensor coverage, deployment and networked solutions in 2d sensing model environments for wsns ineffective and inapplicable in solving the wireless video sensor network (wvsn) issues for 3d indoor space, thus calling for novel solutions. In this dissertation, we propose optimization techniques and develop solutions that will address the coverage, deployment and network issues associated within wireless video sensor networks for a 3d indoor environment. We first model the general problem in a continuous 3d space to minimize the total number of required video sensors to monitor a given 3d indoor region. We then convert it into a discrete version problem by incorporating 3d grids, which can achieve arbitrary approximation precision by adjusting the grid granularity. Due in part to the uniqueness of the visual sensor directional sensing range, we propose to exploit the directional feature to determine the optimal angular-coverage of each deployed visual sensor. Thus, we propose to deploy the visual sensors from divergent directional angles and further extend k-coverage to ``k-angular-coverage\u27\u27, while ensuring connectivity within the network. We then propose a series of mechanisms to handle obstacles in the 3d environment. We develop efficient greedy heuristic solutions that integrate all these aforementioned considerations one by one and can yield high quality results. Based on this, we also propose enhanced depth first search (dfs) algorithms that can not only further improve the solution quality, but also return optimal results if given enough time. Our extensive simulations demonstrate the superiority of both our greedy heuristic and enhanced dfs solutions. Finally, this dissertation discusses some future research directions such as in-network traffic routing and scheduling issues

Autonomous Underwater Vehicle: 5G Network Design and Simulation Based on Mimetic Technique Control System

The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay and higher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memetic algorithm-based AUV monitoring system for the underwater environment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to increase the USAN performance. The A5GMEMETIC model data collection through the dynamic unaware clustering model minimizes energy consumption. The A5GMemetic optimizes the location of the nodes in the underwater environment for the optimal data path estimation for the data transmission in the network. Simulation analysis is performed comparatively with the proposed A5Gmemetic with the conventional AEDG, DGS, and HAMA models. The comparative analysis expressed that the proposed A5GMeMEMETIC model exhibits the ~12% increased packet delivery ratio (PDR), ~9% reduced delay and ~8% improved network lifetime

IoT for measurements and measurements for IoT

The thesis is framed in the broad strand of the Internet of Things, providing two parallel paths. On one hand, it deals with the identification of operational scenarios in which the IoT paradigm could be innovative and preferable to pre-existing solutions, discussing in detail a couple of applications. On the other hand, the thesis presents methodologies to assess the performance of technologies and related enabling protocols for IoT systems, focusing mainly on metrics and parameters related to the functioning of the physical layer of the systems

Algorithms for data-gathering in wireless sensor networks

Wireless sensor networks consist of a large number of small battery powered sensor nodes with limited energy resources which are responsible for sensing, processing, and transmitting the monitored data. Once deployed, the sensor nodes are normally inaccessible to the user, and thus replacement of the battery is generally not feasible. A major concern in designing and operating dense Wireless Sensor Networks (WSNs) is the energy-efficiency. Hierarchical clustering and cross-layer optimization are widely accepted as effective techniques to ameliorate this concern. We propose two different novel energy efficient algorithms to gather data from sensor nodes. Energy-Efficient Media Access Control (EE-MAC) protocol is the first algorithm, which has excellent scalability and performs well for both small and large sensor networks. We will also provide a theoretical analysis of the protocol and give guidelines on how to find the optimal protocol parameters such as the number of clusters. In addition, we develop and analyze a novel and scalable Spiraled Algorithm for Data-gathering (SAD) that periodically selects cluster heads according to their geographic locations and residual energy by sorting nodes on virtual spirals. Theoretical analysis and simulation results show that SAD can achieve as much as a factor of three prolonging network lifetime compared with other conventional protocols like LEACH especially when the network is large. Moreover, SAD is also able to distribute energy dissipation evenly throughout the sensors such that 80% of the nodes run out of batteries in the last 20% of the network lifetime