6,482 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Breathfinding: A Wireless Network that Monitors and Locates Breathing in a Home
This paper explores using RSS measurements on many links in a wireless
network to estimate the breathing rate of a person, and the location where the
breathing is occurring, in a home, while the person is sitting, laying down,
standing, or sleeping. The main challenge in breathing rate estimation is that
"motion interference", i.e., movements other than a person's breathing,
generally cause larger changes in RSS than inhalation and exhalation. We
develop a method to estimate breathing rate despite motion interference, and
demonstrate its performance during multiple short (3-7 minute) tests and during
a longer 66 minute test. Further, for the same experiments, we show the
location of the breathing person can be estimated, to within about 2 m average
error in a 56 square meter apartment. Being able to locate a breathing person
who is not otherwise moving, without calibration, is important for applications
in search and rescue, health care, and security
Goodbye, ALOHA!
©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The vision of the Internet of Things (IoT) to interconnect and Internet-connect everyday people, objects, and machines poses new challenges in the design of wireless communication networks. The design of medium access control (MAC) protocols has been traditionally an intense area of research due to their high impact on the overall performance of wireless communications. The majority of research activities in this field deal with different variations of protocols somehow based on ALOHA, either with or without listen before talk, i.e., carrier sensing multiple access. These protocols operate well under low traffic loads and low number of simultaneous devices. However, they suffer from congestion as the traffic load and the number of devices increase. For this reason, unless revisited, the MAC layer can become a bottleneck for the success of the IoT. In this paper, we provide an overview of the existing MAC solutions for the IoT, describing current limitations and envisioned challenges for the near future. Motivated by those, we identify a family of simple algorithms based on distributed queueing (DQ), which can operate for an infinite number of devices generating any traffic load and pattern. A description of the DQ mechanism is provided and most relevant existing studies of DQ applied in different scenarios are described in this paper. In addition, we provide a novel performance evaluation of DQ when applied for the IoT. Finally, a description of the very first demo of DQ for its use in the IoT is also included in this paper.Peer ReviewedPostprint (author's final draft
Dynamic Channel Access Scheme for Interference Mitigation in Relay-assisted Intra-WBANs
This work addresses problems related to interference mitigation in a single
wireless body area network (WBAN). In this paper, We propose a distributed
\textit{C}ombined carrier sense multiple access with collision avoidance
(CSMA/CA) with \textit{F}lexible time division multiple access (\textit{T}DMA)
scheme for \textit{I}nterference \textit{M}itigation in relay-assisted
intra-WBAN, namely, CFTIM. In CFTIM scheme, non interfering sources
(transmitters) use CSMA/CA to communicate with relays. Whilst, high interfering
sources and best relays use flexible TDMA to communicate with coordinator (C)
through using stable channels. Simulation results of the proposed scheme are
compared to other schemes and consequently CFTIM scheme outperforms in all
cases. These results prove that the proposed scheme mitigates interference,
extends WBAN energy lifetime and improves the throughput. To further reduce the
interference level, we analytically show that the outage probability can be
effectively reduced to the minimal.Comment: 2015 IEEE International Conference on Protocol Engineering (ICPE) and
International Conference on New Technologies of Distributed Systems (NTDS),
Paris, France. arXiv admin note: text overlap with arXiv:1602.0865
Synchronous wearable wireless body sensor network composed of autonomous textile nodes
A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system
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
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
- …