Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

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

Novel Clustering Techniques in Wireless Sensor Networks – A Survey

A study of Wireless Sensor Networks has been growing tremendously these days. Wireless Sensor Networks play a major role in various fields ranging from smart homes to health care. WSN’s operate independently in remote places. Because of tiny size of the nodes in such type of networks, they have a limited number of resources in terms of energy and power. Basically, sensor networks can be classified into flat and cluster based Wireless Sensor Networks. But, Clustering based Sensor Networks play a major role in reducing the energy consumption in Wireless Sensor Networks. Clustering also focuses on solving the No.s that arise during transmission of data. Clustering will group nodes into clusters and elects Cluster Heads for all clusters in the network. Then the nodes sense data and send that data to cluster head where the aggregation of data will take place. This paper focuses on various novel clustering techniques that improve the network’s lifetime

Fuzzy enhanced Cluster based Energy Efficient Multicast Protocol for Increasing Network Lifetime in WSN

99–102Wireless Sensor Networks (CWSN) consists of sensor node which is mobile roaming inside and outside the network region. The difficulty in existing models observed is to identify the best routes for forwarding packets. If the balancing of packet arrivals and energy conservation is not achieved, it may lead to reduction of network lifetime. In our research work, Fuzzy enhanced Cluster based Energy Efficient Multicast Protocol (FCEEMP) is developed based on three aspects. First one, the establishment of multicast routing based on the calculation of best route metric and average reliability metric. Second, the cluster is formed based on node stability and route capability. Three set of nodes are formed in the cluster network model i.e. sensor node, cluster member and Cluster Head (CH) to estimate energy consumption. Third, enhancement of fuzzy model is implemented to produce optimal energy and the value of network lifetime. From the simulation analysis, proposed protocol achieves better improvement over existing schemes

A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and Energy- Aware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

A Wildfire Prediction Based on Fuzzy Inference System for Wireless Sensor Networks

The study of forest fires has been traditionally considered as an important application due to the inherent danger that this entails. This phenomenon takes place in hostile regions of difficult access and large areas. Introduction of new technologies such as Wireless Sensor Networks (WSNs) has allowed us to monitor such areas. In this paper, an intelligent system for fire prediction based on wireless sensor networks is presented. This system obtains the probability of fire and fire behavior in a particular area. This information allows firefighters to obtain escape paths and determine strategies to fight the fire. A firefighter can access this information with a portable device on every node of the network. The system has been evaluated by simulation analysis and its implementation is being done in a real environment.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570

K-Means and Fuzzy based Hybrid Clustering Algorithm for WSN

Wireless Sensor Networks (WSN) acquired a lotof attention due to their widespread use in monitoring hostileenvironments, critical surveillance and security applications. Inthese applications, usage of wireless terminals also has grownsignificantly. Grouping of Sensor Nodes (SN) is called clusteringand these sensor nodes are burdened by the exchange of messagescaused due to successive and recurring re-clustering, whichresults in power loss. Since most of the SNs are fitted with nonrechargeablebatteries, currently researchers have been concentratingtheir efforts on enhancing the longevity of these nodes. Forbattery constrained WSN concerns, the clustering mechanism hasemerged as a desirable subject since it is predominantly good atconserving the resources especially energy for network activities.This proposed work addresses the problem of load balancingand Cluster Head (CH) selection in cluster with minimum energyexpenditure. So here, we propose hybrid method in which clusterformation is done using unsupervised machine learning based kmeansalgorithm and Fuzzy-logic approach for CH selection