4,649 research outputs found
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
Survey on Data-Centric based Routing Protocols for Wireless Sensor Networks
The great concern for energy that grew with the technological advances in the
field of networks and especially in sensor network has triggered various
approaches and protocols that relate to sensor networks. In this context, the
routing protocols were of great interest. The aim of the present paper is to
discuss routing protocols for sensor networks. This paper will focus mainly on
the discussion of the data-centric approach (COUGAR, rumor, SPIN, flooding and
Gossiping), while shedding light on the other approaches occasionally. The
functions of the nodes will be discussed as well. The methodology selected for
this paper is based on a close description and discussion of the protocol. As a
conclusion, open research questions and limitations are proposed to the reader
at the end of this paper
AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications
Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability
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
A survey on subjecting electronic product code and non-ID objects to IP identification
Over the last decade, both research on the Internet of Things (IoT) and
real-world IoT applications have grown exponentially. The IoT provides us with
smarter cities, intelligent homes, and generally more comfortable lives.
However, the introduction of these devices has led to several new challenges
that must be addressed. One of the critical challenges facing interacting with
IoT devices is to address billions of devices (things) around the world,
including computers, tablets, smartphones, wearable devices, sensors, and
embedded computers, and so on. This article provides a survey on subjecting
Electronic Product Code and non-ID objects to IP identification for IoT
devices, including their advantages and disadvantages thereof. Different
metrics are here proposed and used for evaluating these methods. In particular,
the main methods are evaluated in terms of their: (i) computational overhead,
(ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether
applicable to already ID-based objects and presented in tabular format.
Finally, the article proves that this field of research will still be ongoing,
but any new technique must favorably offer the mentioned five evaluative
parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports,
Wiley, 2020 (Open Access
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
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