A Distributed Query Processing Engine

Wireless sensor networks (WSNs) are formed of tiny, highly energy-constrained sensor nodes that are equipped with wireless transceivers. They may be mobile and are usually deployed in large numbers in unfamiliar environments. The nodes communicate with one another by autonomously creating ad-hoc networks which are subsequently used to gather sensor data. WSNs also process the data within the network itself and only forward the result to the requesting node. This is referred to as in-network data aggregation and results in the substantial reduction of the amount of data that needs to be transmitted by any single node in the network. In this paper we present a framework for a distributed query processing engine (DQPE) which would allow sensor nodes to examine incoming queries and autonomously perform query optimisation using information available locally. Such qualities make a WSN the perfect tool to carryout environmental\ud monitoring in future planetary exploration missions in a reliable and cost effective manner

Towards Intelligent and Generic LBS for Drivers and Mobile Users

In this talk I will focus on Location-Based Services (LBS) for hybrid networks composed of both vehicles and mobile users. The motivation is the interest of studying data management solutions that take into account a generic environment where different types of moving objects share different types of data and possibly using different communication technologies (ad hoc wireless communications forming a pure mobile P2P network, hybrid mobile P2P network with support infrastructure nodes, wide-area communications like 3G, etc.). I will start by summarizing some data management challenges for vehicular networks, related to the exchange of events (efficient and effective content-based data dissemination for push-based data access), query processing (pull-based data access by using query dissemination or mobile agent technology), data item relevance evaluation, management of information about scarce resources (like available parking spaces or charge stations for electric vehicles), semantic data management, automatic knowledge extraction from the data items, multimedia data management, incentives, and trust. Then ..

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

Service and device discovery of nodes in a wireless sensor network

Emerging wireless communication standards and more capable sensors and actuators have pushed further development of wireless sensor networks. Deploying a large number of sensor\ud nodes requires a high-level framework enabling the devices to present themselves and the resources they hold. The device and the resources can be described as services, and in this paper, we review a number of well-known service discovery protocols. Bonjour stands out with its auto-configuration, distributed architecture, and sharing of resources. We also present a lightweight implementation in order to demonstrate that an emerging standards-based device and service discovery protocol can actually be deployed on small wireless sensor nodes