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

Cross-Sender Bit-Mixing Coding

Scheduling to avoid packet collisions is a long-standing challenge in networking, and has become even trickier in wireless networks with multiple senders and multiple receivers. In fact, researchers have proved that even {\em perfect} scheduling can only achieve $\mathbf{R} = O(\frac{1}{\ln N})$. Here $N$ is the number of nodes in the network, and $\mathbf{R}$ is the {\em medium utilization rate}. Ideally, one would hope to achieve $\mathbf{R} = \Theta(1)$, while avoiding all the complexities in scheduling. To this end, this paper proposes {\em cross-sender bit-mixing coding} ({\em BMC}), which does not rely on scheduling. Instead, users transmit simultaneously on suitably-chosen slots, and the amount of overlap in different user's slots is controlled via coding. We prove that in all possible network topologies, using BMC enables us to achieve $\mathbf{R}=\Theta(1)$. We also prove that the space and time complexities of BMC encoding/decoding are all low-order polynomials.Comment: Published in the International Conference on Information Processing in Sensor Networks (IPSN), 201

An ACO and Mobile Sink based Algorithm for Improvement of ML-MAC for Wsns using Compressive Sensing

WSN is becoming key subject of research in computational basic principle because of its great deal of applications. ACO( Ant Colony Optimization) constructs the redirecting or routing tree via a method by which, for every single circular or round, Base Station (BS) chooses the root node in addition to shows the following substitute for every node. In order to prevail over the actual constraints with the sooner work a new increased method proposed in this research work. The proposed method has the capacity to prevail over the constraints of ACO routing protocol using the principle with reactivity, mobile sink and also the compressive sensing technique. In this paper we measure the main parameters that affect the wsn that are network lifetime, packets dropped, throughput, end to end delay and remaining energy for proposed algorithm and simulation results have shown that the proposed algorithm is highly effective

Next Generation M2M Cellular Networks: Challenges and Practical Considerations

In this article, we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity problem, support for low-power, low-cost, and numerous number of devices. As being an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. Cognitive radio concept has emerged to significantly tackle the spectrum under-utilization or scarcity problem. Heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and engineering knowledge of cognitive radios, heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks

Wireless Technology for Monitoring Site-specific Landslide in Vietnam

Climate change has caused an increasing number of landslides, especially in the mountainous provinces of Vietnam, resulting in the destruction of vital transport and other infrastructure. Current monitoring and forecasting systems of the meteorology department cannot deliver accurate and reliable forecasts for weather events and issue timely warnings. This paper describes the development of a simple, low cost, and efficient system for monitoring and warning landslide in real-time. The authors focus on the use of wireless and related technologies in the implementation of a technical solution and some of the problems of the wireless sensor network (WSN) related to power consumption. Promising compressed sensing (CS) based solution for landslide monitoring is discussed and evaluated in the paper

Efficient energy for one node and multi-nodes of wireless body area network

Compression sensing approaches have been used extensively with the idea of overcoming the limitations of traditional sampling theory and applying the concept of pressure during the sensing procedure. Great efforts have been made to develop methods that would allow data to be sampled in compressed form using a much smaller number of samples. Wireless body area networks (WBANs) have been developed by researchers through the creation of the network and the use of miniature equipment. Small structural factors, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power are needed to hold the wireless sensor through lightweight, implantable, and sharing communication tools wireless body area network. Thus, the proposed system provides a brief idea of the use of WBAN using IEEE 802.15.4 with compression sensing technologies. To build a health system that helps people maintain their health without going to the hospital and get more efficient energy through compression sensing, more efficient energy is obtained and thus helps the sensor battery last longer, and finally, the proposed health system will be more efficient energy, less energy-consuming, less expensive and more throughput

Bioelectronic Sensor Nodes for Internet of Bodies

Energy-efficient sensing with Physically-secure communication for bio-sensors on, around and within the Human Body is a major area of research today for development of low-cost healthcare, enabling continuous monitoring and/or secure, perpetual operation. These devices, when used as a network of nodes form the Internet of Bodies (IoB), which poses certain challenges including stringent resource constraints (power/area/computation/memory), simultaneous sensing and communication, and security vulnerabilities as evidenced by the DHS and FDA advisories. One other major challenge is to find an efficient on-body energy harvesting method to support the sensing, communication, and security sub-modules. Due to the limitations in the harvested amount of energy, we require reduction of energy consumed per unit information, making the use of in-sensor analytics/processing imperative. In this paper, we review the challenges and opportunities in low-power sensing, processing and communication, with possible powering modalities for future bio-sensor nodes. Specifically, we analyze, compare and contrast (a) different sensing mechanisms such as voltage/current domain vs time-domain, (b) low-power, secure communication modalities including wireless techniques and human-body communication, and (c) different powering techniques for both wearable devices and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which has been accepted for Publication in Volume 25 of the Annual Review of Biomedical Engineering (2023). Only Personal Use is Permitte

Sensor and actuator networks for smart grid

As introduced in the previous chapters, compared to traditional power grid, Smart Grid (SG) enjoys various advantages. To realize these advantages, Sensor and Actuator Networks (SANET) play a key role. In this chapter, we focus on SANET for SG. We study the composition and characteristics of SANET, identify the major applications of SANET in SG, highlight the major design issues and implementation challenges, and propose some innovative mechanisms to address these challenges. The effectiveness of the proposed schemes is verified and demonstrated with a case study of Energy Management System (EMS).published_or_final_versio

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future