99,659 research outputs found
A Smart Game for Data Transmission and Energy Consumption in the Internet of Things
The current trend in developing smart technology for the Internet of Things (IoT) has motivated a lot of research interest in optimizing data transmission or minimizing energy consumption, but with little evidence of proposals for achieving both objectives in a single model. Using the concept of game theory, we develop a new MAC protocol for IEEE 802.15.4 and IoT networks in which we formulate a novel expression for the players' utility function and establish a stable Nash equilibrium (NE) for the game. The proposed IEEE 802.15.4 MAC protocol is modeled as a smart game in which analytical expressions are derived for channel access probability, data transmission probability, and energy used. These analytical expressions are used in formulating an optimization problem (OP) that maximizes data transmission and minimizes energy consumption by nodes. The analysis and simulation results suggest that the proposed scheme is scalable and achieves better performance in terms of data transmission, energy-efficiency, and longevity, when compared with the default IEEE 802.15.4 access mechanism.Peer reviewe
Cognitive Connectivity Resilience in Multi-layer Remotely Deployed Mobile Internet of Things
Enabling the Internet of things in remote areas without traditional
communication infrastructure requires a multi-layer network architecture. The
devices in the overlay network are required to provide coverage to the underlay
devices as well as to remain connected to other overlay devices. The
coordination, planning, and design of such two-layer heterogeneous networks is
an important problem to address. Moreover, the mobility of the nodes and their
vulnerability to adversaries pose new challenges to the connectivity. For
instance, the connectivity of devices can be affected by changes in the
network, e.g., the mobility of the underlay devices or the unavailability of
overlay devices due to failure or adversarial attacks. To this end, this work
proposes a feedback based adaptive, self-configurable, and resilient framework
for the overlay network that cognitively adapts to the changes in the network
to provide reliable connectivity between spatially dispersed smart devices. Our
results show that if sufficient overlay devices are available, the framework
leads to a connected configuration that ensures a high coverage of the mobile
underlay network. Moreover, the framework can actively reconfigure itself in
the event of varying levels of device failure.Comment: To appear in IEEE Global Communications Conference (Globecom 2017
Evolutionary Game Theory Perspective on Dynamic Spectrum Access Etiquette
In this paper, we describe the long-term evolution of societies of secondary users in dynamic spectrum access networks. Such an understanding is important to help us anticipate future trends in the organization of large-scale distributed networked deployments. Such deployments are expected to arise in support of a wide variety of applications, including vehicular networks and the Internet of Things. Two new biologically-inspired spectrum access strategies are presented here, and compared with a random access baseline strategy. The proposed strategies embody a range of plausible assumptions concerning the sensing capabilities and social characteristics of individual secondary users. Considering these strategies as the basis of a game against the field, we use replicator dynamics within an evolutionary game-theoretic analysis to derive insights into the physical conditions necessary for each of the strategies to be evolutionarily stable. Somewhat surprisingly, we find that the physical channel conditions almost always uniquely determine which one of the three (pure) strategies is selected, and that no mixed strategy ever survives. We show that social tendencies naturally become advantageous for secondary users as they find themselves situated in network environments with heterogeneous channel resources. Hardware test-bed experiments confirm the validity of the analytic conclusions. Taken together, these results predict the emergence of social behavior in the spectrum access etiquette of secondary users as cognitive radio technology continues to advance and improve. The experimental results show an increase in the throughput of up to 90%, when strategy evolution is continuously operational, compared with any static strategy. We present use cases to envision the potential application of the proposed evolutionary framework in real-world scenarios
Understanding the limits of LoRaWAN
The quick proliferation of LPWAN networks, being LoRaWAN one of the most
adopted, raised the interest of the industry, network operators and facilitated
the development of novel services based on large scale and simple network
structures. LoRaWAN brings the desired ubiquitous connectivity to enable most
of the outdoor IoT applications and its growth and quick adoption are real
proofs of that. Yet the technology has some limitations that need to be
understood in order to avoid over-use of the technology. In this article we aim
to provide an impartial overview of what are the limitations of such
technology, and in a comprehensive manner bring use case examples to show where
the limits are
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
Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions
In this paper, we propose and evaluate different learning strategies based on
Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices
to improve their access to the network and their autonomy, while taking into
account the impact of encountered radio collisions. For that end, several
heuristics employing Upper-Confident Bound (UCB) algorithms are examined, to
explore the contextual information provided by the number of retransmissions.
Our results show that approaches based on UCB obtain a significant improvement
in terms of successful transmission probabilities. Furthermore, it also reveals
that a pure UCB channel access is as efficient as more sophisticated learning
strategies.Comment: The source code (MATLAB or Octave) used for the simula-tions and the
figures is open-sourced under the MIT License,
atBitbucket.org/scee\_ietr/ucb\_smart\_retran
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