265 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
A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms
Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio
Clustering objectives in wireless sensor networks: A survey and research direction analysis
Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio
Resilient Wireless Sensor Networks Using Topology Control: A Review
Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs
Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks
Existing anomaly and intrusion detection schemes of wireless sensor networks
have mainly focused on the detection of intrusions. Once the intrusion is
detected, an alerts or claims will be generated. However, any unidentified
malicious nodes in the network could send faulty anomaly and intrusion claims
about the legitimate nodes to the other nodes. Verifying the validity of such
claims is a critical and challenging issue that is not considered in the
existing cooperative-based distributed anomaly and intrusion detection schemes
of wireless sensor networks. In this paper, we propose a validation algorithm
that addresses this problem. This algorithm utilizes the concept of
intrusion-aware reliability that helps to provide adequate reliability at a
modest communication cost. In this paper, we also provide a security resiliency
analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure
QoS BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK
A Wireless Sensor Networks (WSN) is composed of a large number of low-powered
sensor nodes that are randomly deployed to collect environmental data. In a WSN,
because of energy scarceness, energy efficient gathering of sensed information is one
of the most critical issues. Thus, most of the WSN routing protocols found in the
literature have considered energy awareness as a key design issue. Factors like
throughput, latency and delay are not considered as critical issues in these protocols.
However, emerging WSN applications that involve multimedia and imagining sensors
require end-to-end delay within acceptable limits. Hence, in addition to energy
efficiency, the parameters (delay, packet loss ratio, throughput and coverage) have
now become issues of primary concern. Such performance metrics are usually
referred to as the Quality of Service (QoS) in communication systems. Therefore, to
have efficient use of a sensor node’s energy, and the ability to transmit the imaging
and multimedia data in a timely manner, requires both a QoS based and energy
efficient routing protocol. In this research work, a QoS based energy efficient routing
protocol for WSN is proposed. To achieve QoS based energy efficient routing, three
protocols are proposed, namely the QoS based Energy Efficient Clustering (QoSEC)
for a WSN, the QoS based Energy Efficient Sleep/Wake Scheduling (QoSES) for a
WSN, and the QoS based Energy Efficient Mobile Sink (QoSEM) based Routing for a
Clustered WSN.
Firstly, in the QoSEC, to achieve energy efficiency and to prolong
network/coverage lifetime, some nodes with additional energy resources, termed as
super-nodes, in addition to normal capability nodes, are deployed. Multi-hierarchy
clustering is done by having super-nodes (acting as a local sink) at the top tier, cluster
head (normal node) at the middle tier, and cluster member (normal node) at the lowest
tier in the hierarchy. Clustering within normal sensor nodes is done by optimizing the
network/coverage lifetime through a cluster-head-selection algorithm and a
sleep/wake scheduling algorithm. QoSEC resolves the hot spot problem and prolongs
network/coverage lifetime.
Secondly, the QoSES addressed the delay-minimization problem in sleep/wake
scheduling for event-driven sensor networks for delay-sensitive applications. For this
purpose, QoSES assigns different sleep/wake intervals (longer wake interval) to
potential overloaded nodes, according to their varied traffic load requirement defined
a) by node position in the network, b) by node topological importance, and c) by
handling burst traffic in the proximity of the event occurrence node. Using these
heuristics, QoSES minimizes the congestion at nodes having heavy traffic loads and
ultimately reduces end-to-end delay while maximizing the throughput.
Lastly, the QoSEM addresses hot spot problem, delay minimization, and QoS
assurance. To address hot-spot problem, mobile sink is used, that move in the network
to gather data by virtue of which nodes near to the mobile sink changes with each
movement, consequently hot spot problem is minimized. To achieve delay
minimization, static sink is used in addition to the mobile sink. Delay sensitive data is
forwarded to the static sink, while the delay tolerant data is sent through the mobile
sink. For QoS assurance, incoming traffic is divided into different traffic classes and
each traffic class is assigned different priority based on their QoS requirement
(bandwidth, delay) determine by its message type and content. Furthermore, to
minimize delay in mobile sink data gathering, the mobile sink is moved throughout
the network based on the priority messages at the nodes. Using these heuristics,
QoSEM incur less end-to-end delay, is energy efficient, as well as being able to
ensure QoS.
Simulations are carried out to evaluate the performance of the proposed protocols
of QoSEC, QoSES and QoSEM, by comparing their performance with the established
contemporary protocols. Simulation results have demonstrated that when compared
with contemporary protocols, each of the proposed protocol significantly prolong the
network and coverage lifetime, as well as improve the other QoS routing parameters,
such as delay, packet loss ratio, and throughput
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