A tool for rapid MAC protocols prototyping and designing for wireless sensor networks

Wireless Sensor Networks (WSNs)consists of several resource constrained sensor nodes distributed over an specific geographical area. WSNs are typically energy constraint due to the fact the sensor nodes are battery powered. Medium Access Control (MAC) protocols used in WSNs are usually designed to be power aware, i.e., they are more energy efficient than MAC protocols used for other ad-hoc wireless networks such as IEEE 802.11 [2]; to increase the lifetime of the nodes. Traditional MAC protocol implementations are done for specific hardware platforms using a monolithic approach. Therefore, it is very difficult to port from one platform to another without modifying the whole implementation protocol. This reduces code reusage and increases the implementation efforts. We have designed and implemented a toolchain which allows to design and prototype MAC protocols for WSNs in a simple manner. In addition, it allows non-specific sensors users to implement and execute them in sensor nodes without worrying about technical specifications of the platforms. The toolchain has been implemented in TinyOS using a component-based design. Special care has been taken to ensure hardware independence of the protocol implementations described in this thesis has been integrated with [1] to allow runtime reconfiguration of MAC protocols. We have evaluated our toolchain against monolithic implementations in terms of memory consumption and execution time. The results show that the toolchain introduces an acceptable memory and execution time overhead, less than 5 %, compared to the monolithic approach and substantially eases the implementation efforts

The ContikiMAC Radio Duty Cycling Protocol

Low-power wireless devices must keep their radio transceivers off as much as possible to reach a low power consumption, but must wake up often enough to be able to receive communication from their neighbors. This report describes the ContikiMAC radio duty cycling mechanism, the default radio duty cycling mechanism in Contiki 2.5, which uses a power efficient wake-up mechanism with a set of timing constraints to allow device to keep their transceivers off. With ContikiMAC, nodes can participate in network communication yet keep their radios turned off for roughly 99% of the time. This report describes the ContikiMAC mechanism, measures the energy consumption of individual ContikiMAC operations, and evaluates the efficiency of the fast sleep and phase-lock optimizations

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PluralisMAC: a generic multi-MAC framework for heterogeneous, multiservice wireless networks, applied to smart containers

Developing energy-efficient MAC protocols for lightweight wireless systems has been a challenging task for decades because of the specific requirements of various applications and the varying environments in which wireless systems are deployed. Many MAC protocols for wireless networks have been proposed, often custom-made for a specific application. It is clear that one MAC does not fit all the requirements. So, how should a MAC layer deal with an application that has several modes (each with different requirements) or with the deployment of another application during the lifetime of the system? Especially in a mobile wireless system, like Smart Monitoring of Containers, we cannot know in advance the application state (empty container versus stuffed container). Dynamic switching between different energy-efficient MAC strategies is needed. Our architecture, called PluralisMAC, contains a generic multi-MAC framework and a generic neighbour monitoring and filtering framework. To validate the real-world feasibility of our architecture, we have implemented it in TinyOS and have done experiments on the TMote Sky nodes in the w-iLab.t testbed. Experimental results show that dynamic switching between MAC strategies is possible with minimal receive chain overhead, while meeting the various application requirements (reliability and low-energy consumption)

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

Comparison of CSMA based MAC protocols of wireless sensor networks

Energy conservation has been an important area of interest in Wireless Sensor networks (WSNs). Medium Access Control (MAC) protocols play an important role in energy conservation. In this paper, we describe CSMA based MAC protocols for WSN and analyze the simulation results of these protocols. We implemented S-MAC, T-MAC, B-MAC, B-MAC+, X-MAC, DMAC and Wise-MAC in TOSSIM, a simulator which unlike other simulators simulates the same code running on real hardware. Previous surveys mainly focused on the classification of MAC protocols according to the techniques being used or problem dealt with and presented a theoretical evaluation of protocols. This paper presents the comparative study of CSMA based protocols for WSNs, showing which MAC protocol is suitable in a particular environment and supports the arguments with the simulation results. The comparative study can be used to find the best suited MAC protocol for wireless sensor networks in different environments.Comment: International Journal of AdHoc Network Systems, Volume 2, Number 2, April 201