Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted

Coping with spectrum and energy scarcity in Wireless Networks: a Stochastic Optimization approach to Cognitive Radio and Energy Harvesting

In the last decades, we have witnessed an explosion of wireless communications and networking, spurring a great interest in the research community. The design of wireless networks is challenged by the scarcity of resources, especially spectrum and energy. In this thesis, we explore the potential offered by two novel technologies to cope with spectrum and energy scarcity: Cognitive Radio (CR) and Energy Harvesting (EH). CR is a novel paradigm for improving the spectral efficiency in wireless networks, by enabling the coexistence of an incumbent legacy system and an opportunistic system with CR capability. We investigate a technique where the CR system exploits the temporal redundancy introduced by the Hybrid Automatic Retransmission reQuest (HARQ) protocol implemented by the legacy system to perform interference cancellation, thus enhancing its own throughput. Recently, EH has been proposed to cope with energy scarcity in Wireless Sensor Networks (WSNs). Devices with EH capability harvest energy from the environment, e.g., solar, wind, heat or piezo-electric, to power their circuitry and to perform data sensing, processing and communication tasks. Due to the random energy supply, how to best manage the available energy is an open research issue. In the second part of this thesis, we design control policies for EH devices, and investigate the impact of factors such as the finite battery storage, time-correlation in the EH process and battery degradation phenomena on the performance of such systems. We cast both paradigms in a stochastic optimization framework, and investigate techniques to cope with spectrum and energy scarcity by opportunistically leveraging interference and ambient energy, respectively, whose benefits are demonstrated both by theoretical analysis and numerically. As an additional topic, we investigate the issue of channel estimation in UltraWide-Band (UWB) systems. Due to the large transmission bandwidth, the channel has been typically modeled as sparse. However, some propagation phenomena, e.g., scattering from rough surfaces and frequency distortion, are better modeled by a diffuse channel. We propose a novel Hybrid Sparse/Diffuse (HSD) channel model which captures both components, and design channel estimators based on it

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Fixed-complexity quantum-assisted multi-user detection for CDMA and SDMA

In a system supporting numerous users the complexity of the optimal Maximum Likelihood Multi-User Detector (ML MUD) becomes excessive. Based on the superimposed constellations of K users, the ML MUD outputs the specific multilevel K-user symbol that minimizes the Euclidean distance with respect to the faded and noise-contaminated received multi-level symbol. Explicitly, the Euclidean distance is considered as the Cost Function (CF). In a system supporting K users employing M-ary modulation, the ML MUD uses MK CF evaluations (CFE) per time slot. In this contribution we propose an Early Stopping-aided Durr-Høyer algorithm-based Quantum-assisted MUD (ES-DHA QMUD) based on two techniques for achieving optimal ML detection at a low complexity. Our solution is also capable of flexibly adjusting the QMUD's performance and complexity trade-off, depending on the computing power available at the base station. We conclude by proposing a general design methodology for the ES-DHA QMUD in the context of both CDMA and SDMA systems

Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications

Interference management in impulse-radio ultra-wide band networks

We consider networks of impulse-radio ultra-wide band (IR-UWB) devices. We are interested in the architecture, design, and performance evaluation of these networks in a low data-rate, self-organized, and multi-hop setting. IR-UWB is a potential physical layer for sensor networks and emerging pervasive wireless networks. These networks are likely to have no particular infrastructure, might have nodes embedded in everyday life objects and have a size ranging from a few dozen nodes to large-scale networks composed of hundreds of nodes. Their average data-rate is low, on the order of a few megabits per second. IR-UWB physical layers are attractive for these networks because they potentially combine low-power consumption, robustness to multipath fading and to interference, and location/ranging capability. The features of an IR-UWB physical layer greatly differ from the features of the narrow-band physical layers used in existing wireless networks. First, the bandwidth of an IR-UWB physical layer is at least 500 MHz, which is easily two orders of magnitude larger than the bandwidth used by a typical narrow-band physical layer. Second, this large bandwidth implies stringent radio spectrum regulations because UWB systems might occupy a portion of the spectrum that is already in use. Consequently, UWB systems exhibit extremely low power spectral densities. Finally IR-UWB physical layers offer multi-channel capabilities for multiple and concurrent access to the physical layer. Hence, the architecture and design of IR-UWB networks are likely to differ significantly from narrow-band wireless networks. For the network to operate efficiently, it must be designed and implemented to take into account the features of IR-UWB and to take advantage of them. In this thesis, we focus on both the medium access control (MAC) layer and the physical layer. Our main objectives are to understand and determine (1) the architecture and design principles of IR-UWB networks, and (2) how to implement them in practical schemes. In the first part of this thesis, we explore the design space of IR-UWB networks and analyze the fundamental design choices. We show that interference from concurrent transmissions should not be prevented as in protocols that use mutual exclusion (for instance, IEEE 802.11). Instead, interference must be managed with rate adaptation, and an interference mitigation scheme should be used at the physical layer. Power control is useless. Based on these findings, we develop a practical PHY-aware MAC protocol that takes into account the specific nature of IR-UWB and that is able to adapt its rate to interference. We evaluate the performance obtained with this design: It clearly outperforms traditional designs that, instead, use mutual exclusion or power control. One crucial aspect of IR-UWB networks is packet detection and timing acquisition. In this context, a network design choice is whether to use a common or private acquisition preamble for timing acquisition. Therefore, we evaluate how this network design issue affects the network throughput. Our analysis shows that a private acquisition preamble yields a tremendous increase in throughput, compared with a common acquisition preamble. In addition, simulations on multi-hop topologies with TCP flows demonstrate that a network using private acquisition preambles has a stable throughput. On the contrary, using a common acquisition preamble exhibits an effect similar to exposed terminal issues in 802.11 networks: the throughput is severely degraded and flow starvation might occur. In the second part of this thesis, we are interested in IEEE 802.15.4a, a standard for low data-rate, low complexity networks that employs an IR-UWB physical layer. Due to its low complexity, energy detection is appealing for the implementation of practical receivers. But it is less robust to multi-user interference (MUI) than a coherent receiver. Hence, we evaluate the performance of an IEEE 802.15.4a physical layer with an energy detection receiver to find out whether a satisfactory performance is still obtained. Our results show that MUI severely degrades the performance in this case. The energy detection receiver significantly diminishes one of the most appealing benefits of UWB, specifically its robustness to MUI and thus the possibility of allowing for parallel transmissions. This performance analysis leads to the development of an IR-UWB receiver architecture, based on energy detection, that is robust to MUI and adapted to the peculiarities of IEEE 802.15.4a. This architecture greatly improves the performance and entails only a moderate increase in complexity. Finally, we present the architecture of an IR-UWB physical layer implementation in ns-2, a well-known network simulator. This architecture is generic and allows for the simulation of several multiple-access physical layers. In addition, it comprises a model of packet detection and timing acquisition. Network simulators also need to have efficient algorithms to accurately compute bit or packet error rates. Hence, we present a fast algorithm to compute the bit error rate of an IR-UWB physical layer in a network setting with MUI. It is based on a novel combination of large deviation theory and importance sampling