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

Classification of networks-on-chip in the context of analysis of promising self-organizing routing algorithms

This paper contains a detailed analysis of the current state of the network-on-chip (NoC) research field, based on which the authors propose the new NoC classification that is more complete in comparison with previous ones. The state of the domain associated with wireless NoC is investigated, as the transition to these NoCs reduces latency. There is an assumption that routing algorithms from classical network theory may demonstrate high performance. So, in this article, the possibility of the usage of self-organizing algorithms in a wireless NoC is also provided. This approach has a lot of advantages described in the paper. The results of the research can be useful for developers and NoC manufacturers as specific recommendations, algorithms, programs, and models for the organization of the production and technological process.Comment: 10 p., 5 fig. Oral presentation on APSSE 2021 conferenc

Time constrained fault tolerance and management framework for k-connected distributed wireless sensor networks based on composite event detection

Wireless sensor nodes themselves are exceptionally complex systems where a variety of components interact in a complex way. In enterprise scenarios it becomes highly important to hide the details of the underlying sensor networks from the applications and to guarantee a minimum level of reliability of the system. One of the challenges faced to achieve this level of reliability is to overcome the failures frequently faced by sensor networks due to their tight integration with the environment. Failures can generate false information, which may trigger incorrect business processes, resulting in additional costs. Sensor networks are inherently fault prone due to the shared wireless communication medium. Thus, sensor nodes can lose synchrony and their programs can reach arbitrary states. Since on-site maintenance is not feasible, sensor network applications should be local and communication-efficient self-healing. Also, as per my knowledge, no such general framework exist that addresses all the fault issues one may encounter in a WSN, based on the extensive, exhaustive and comprehensive literature survey in the related areas of research. As one of the main goals of enterprise applications is to reduce the costs of business processes, a complete and more general Fault Tolerance and management framework for a general WSN, irrespective of the node types and deployment conditions is proposed which would help to mitigate the propagation of failures in a business environment, reduce the installation and maintenance costs and to gain deployment flexibility to allow for unobtrusive installation

Energy-aware distributed routing algorithm to tolerate network failure in wireless sensor networks

Wireless Sensor Networks are prone to link/node failures due to various environmental hazards such as interference and internal faults in deployed sensor nodes. Such failures can result in a disconnection in part of the network and the sensed data being unable to obtain a route to the sink(s), i.e. a network failure. Network failures potentially degrade the Quality of Service (QoS) of Wireless Sensor Networks (WSNs). It is very difficult to monitor network failures using a manual operator in a harsh or hostile environment. In such environments, communication links can easy fail because of node unequal energy depletion and hardware failure or invasion. Thus it is desirable that deployed sensor nodes are capable of overcoming network failures. In this paper, we consider the problem of tolerating network failures seen by deployed sensor nodes in a WSN. We first propose a novel clustering algorithm for WSNs, termed Distributed Energy Efficient Heterogeneous Clustering (DEEHC) that selects cluster heads according to the residual energy of deployed sensor nodes with the aid of a secondary timer. During the clustering phase, each sensor node finds k-vertex disjoint paths to cluster heads depending on the energy level of its neighbor sensor nodes. We then present a k-Vertex Disjoint Path Routing (kVDPR) algorithm where each cluster head finds k-vertex disjoint paths to the base station and relays their aggregate data to the base station. Furthermore, we also propose a novel Route Maintenance Mechanism (RMM) that can repair k-vertex disjoint paths throughout the monitoring session. The resulting WSNs become tolerant to k-1 failures in the worst case. The proposed scheme has been extensively tested using various network scenarios and compared to the existing state of the art approaches to show the effectiveness of the proposed scheme

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan