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

Surveying Position Based Routing Protocols for Wireless Sensor and Ad-hoc Networks

A focus of the scientific community is to design network oriented position-based routing protocols and this has resulted in a very high number of algorithms, different in approach and performance and each suited only to particular applications. However, though numerous, very few position-based algorithms have actually been adopted for commercial purposes. This article is a survey of almost 50 position-based routing protocols and it comes as an aid in the implementation of this type of routing in various applications which may need to consider the advantages and pitfalls of position-based routing. An emphasis is made on geographic routing, whose notion is clarified as a more restrictive and more efficient type of position-based routing. The protocols are therefore divided into geographic and non-geographic routing protocols and each is characterized according to a number of network design issues and presented in a comparative manner from multiple points of view. The main requirements of current general applications are also studied and, depending on these, the survey proposes a number of protocols for use in particular application areas. This aims to help both researchers and potential users assess and choose the protocol best suited to their interest

Cross-layer design of multi-hop wireless networks

MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

Wireless Sensor Network Based Monitoring System: Implementation, Constraints, and Solution

Wireless Sensor Network (WSN) is a collection of sensors communicating at close range by forming a wireless-based network (wireless). Since 2015 research related to the use of WSN in various health, agriculture, security industry, and other fields has continued to grow. One interesting research case is the use of WSN for the monitoring process by collecting data using sensors placed and distributed in locations based on a wireless system. Sensors with low power, multifunction, supported by a combination of wireless network, microcontroller, memory, operating system, radio communication, and energy source in the form of an integrated battery enable a monitoring process of the monitoring area to run properly. The implementation of the wireless sensor network includes five main parts, namely sender, receiver, wireless transmission media, data/information, network architecture/configuration, and network management. Network management itself includes network configuration management, network performance management, network failure management, network security management, and network financing management. The main obstacles in implementing a wireless sensor network include three things: an effective and efficient data sending/receiving process, limited and easily depleted sensor energy/power, network security, and data security that is vulnerable to eavesdropping and destruction. This paper presents a taxonomy related to the constraints in implementing Wireless Sensor Networks. This paper also presents solutions from existing studies related to the constraints of implementing the WSN. Furthermore, from the results of the taxonomy mapping of these constraints, new gaps were identified related to developing existing research to produce better solutions