Node Heterogeneity for Energy Efficient Synchronization for Wireless Sensor Network

AbstractThe energy of the node in the Wireless Sensor Networks (WSNs) is scare and causes the variation in the lifetime of the network. Also, the throughput and delay of the network depend on how long the network sustains i.e. energy consumption. One way to increase the sustainability of network is the introduction of heterogeneous nodes regarding energy, and the other is to synchronize the local clock of the node with the global clock of the network. In this context, the paper proposes Node Heterogeneity aware Energy Efficient Synchronization Algorithm (NHES). It works on the formation of cluster-based spanning tree (SPT). In the initial stage of the algorithm, the nodes are grouped into the cluster and form the tree. The nodes in the cluster and cluster heads in the network are synchronized with the notion of the global time scale of the network. Also, clock skews may cause the errors and be one of the sources of delay and energy consumption. To minimize the energy consumptions and delay, NHES synchronizes the time slots using TDMA based MAC protocol. The results show that level by level synchronization used in NHES is energy efficient and has less delay as compared to the state-of-the-art solutions

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

Review of Optimization Problems in Wireless Sensor Networks

International audienc

Data Aggregation and Cross-layer Design in WSNs

Over the past few years, advances in electrical engineering have allowed electronic devices to shrink in both size and cost. It has become possible to incorporate environmental sensors into a single device with a microprocessor and memory to interpret the data and wireless transceivers to communicate the data. These sensor nodes have become small and cheap enough that they can be distributed in very large numbers into the area to be monitored and can be considered disposable. Once deployed, these sensor nodes should be able to self-organize themselves into a usable network. These wireless sensor networks, or WSNs, differ from other ad hoc networks mainly in the way that they are used. For example, in ad hoc networks of personal computers, messages are addressed from one PC to another. If a message cannot be routed, the network has failed. In WSNs, data about the environment is requested by the data sink. If any or multiple sensor nodes can return an informative response to this request, the network has succeeded. A network that is viewed in terms of the data it can deliver as opposed to the individual devices that make it up has been termed a data-centric network [26]. The individual sensor nodes may fail to respond to a query, or even die, as long as the final result is valid. The network is only considered useless when no usable data can be delivered. In this thesis, we focus on two aspects. The first is data aggregation with accurate timing control. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Because wireless communication consumes a major amount of the limited battery power for these sensor nodes, we propose to limit the amount of data transmitted by combining redundant and complimentary data as much as possible in order to transmit smaller and fewer messages. By using mathematical models and computer simulations, we will show that our aggregation-focused protocol does, indeed, extend system lifetime. Our secondary focus is a study of cross-layer design. We argue that the extremely specialized use of WSNs should convince us not to adhere to the traditional OSI networking model. Through our experiments, we will show that significant energy savings are possible when a custom cross-layer communication model is used

Aggregation Latency-Energy Tradeoff in Wireless Sensor Networks with Successive Inter- ference Cancellation

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