Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

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

From carbon nanotubes and silicate layers to graphene platelets for polymer nanocomposites

In spite of extensive studies conducted on carbon nanotubes and silicate layers for their polymer-based nanocomposites, the rise of graphene now provides a more promising candidate due to its exceptionally high mechanical performance and electrical and thermal conductivities. The present study developed a facile approach to fabricate epoxy–graphene nanocomposites by thermally expanding a commercial product followed by ultrasonication and solution-compounding with epoxy, and investigated their morphologies, mechanical properties, electrical conductivity and thermal mechanical behaviour. Graphene platelets (GnPs) of 3.5

Publish/subscribe protocol in wireless sensor networks: improved reliability and timeliness

The rapidly-evolving demand of applications using wireless sensor networks in several areas such as building and industrial automation or smart cities, among other, makes it necessary to determine and provide QoS support mechanisms which can satisfy the requirements of applications. In this paper we propose a mechanism that establishes different QoS levels, based on Publish/Subscribe model for wireless networks to meet application requirements, to provide reliable delivery of packet and timeliness. The first level delivers packets in a best effort way. The second one intends to provide reliable packet delivery with a novel approach for Retransmission Timeout (RTO) calculation, which adjusts the RTO depending on the subscriber Packet Delivery Ratio (PDR). The third one provides the same reliable packet delivery as the second one, but in addition, it provides data aggregation trying to be efficient in terms of energy consumption and the use of network bandwidth. The last one provides timeliness in the packet delivery. We evaluate each QoS Level with several performance metrics such as PDR, Message Delivery Ratio, Duplicated and Retransmitted Packet Ratio and Packet Timeliness Ratio to demonstrate that our proposal provides significant improvements based on the increase of the PDR obtained.Peer ReviewedPostprint (author's final draft

An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN