1,955 research outputs found

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

    Full text link
    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

    A Review of Energy Conservation in Wireless Sensor Networks

    Get PDF
    In wireless sensor networks, energy efficiency plays a major role to determine the lifetime of the network. The network is usually powered by a battery which is hard to recharge. Hence, one major challenge in wireless sensor networks is the issue of how to extend the lifetime of sensors to improve the efficiency. In order to reduce the rate at which the network consumes energy, researchers have come up with energy conservation techniques, schemes and protocols to solve the problem. This paper presents a brief overview of wireless sensor networks, outlines some causes of its energy loss and some energy conservation schemes based on existing techniques used in solving the problem of power management. Keywords: Wireless sensor network, Energy conservation, Duty cycling and Energy efficiency

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

    Get PDF
    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

    Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

    Get PDF
    “This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv

    An Adaptable Energy-Efficient Medium Access Control Protocol for Wireless Sensor Networks

    Get PDF
    Wireless networks have become ubiquitous recently and therefore their usefulness has also become more extensive. Wireless sensor networks (WSN) detect environmental information with sensors in remote settings. One problem facing WSNs is the inability to resupply power to these energy-constrained devices due to their remoteness. Therefore to extend a WSN\u27s effectiveness, the lifetime of the network must be increased by making them as energy efficient as possible. An energy efficient medium access control (MAC) can boost a WSN\u27s lifetime. This research creates a MAC protocol called Adaptive sensor Medium Access Control (AMAC) which is based on Sensor Medium Access Control (SMAC) which saves energy by periodically sleeping and not receiving. AMAC adapts to traffic conditions by incorporating multiple duty cycles. Under a high traffic load, AMAC has a short duty cycle and wakes up often. Under a low traffic load, AMAC has a longer duty cycle and wakes up infrequently. The AMAC protocol is simulated in OPNET Modeler using various topologies. AMAC uses 15% less power and 22% less energy per byte than SMAC but doubles the latency. AMAC is promising and further research can decrease its latency and increase its energy efficiency

    Congestion Avoidance Energy Efficient MAC Protocol for Wireless Sensor Networks

    Get PDF
    Wireless Sensor Network (WSNs) are generally energy-constrained and resource-constrained. When multiple simultaneous events occur in densely deployed WSNs, nodes near the base station can become congested, decreasing the network performance. Additionally, multiple nodes may sense an event leading to spatially-correlated contention, further increasing congestion. In order to mitigate the effects of congestion near the base station, an energy-efficient Media Access Control (MAC) protocol that can handle multiple simultaneous events and spatially-correlated contention is needed. Energy efficiency is important and can be achieved using duty cycles but they could degrade the network performance in terms of latency. Existing protocols either provide support for congestion near the base station or for managing spatially-correlated contention. To provide energy-efficiency while maintaining the networks performance under higher traffic load, we propose an energy-efficient congestion-aware MAC protocol. This protocol provides support for congestion near the base station and spatially-correlated contention by employing a traffic shaping approach to manage the arrival times of packets to the layers close to the base station. We implemented our protocol using the ns-2 simulator for evaluating its performance. Results show that our protocol has an improvement in the number of packets received at the base station while consuming less energy

    Effective Node Clustering and Data Dissemination In Large-Scale Wireless Sensor Networks

    Get PDF
    The denseness and random distribution of large-scale WSNs makes it quite difficult to replace or recharge nodes. Energy efficiency and management is a major design goal in these networks. In addition, reliability and scalability are two other major goals that have been identified by researchers as necessary in order to further expand the deployment of such networks for their use in various applications. This thesis aims to provide an energy efficient and effective node clustering and data dissemination algorithm in large-scale wireless sensor networks. In the area of clustering, the proposed research prolongs the lifetime of the network by saving energy through the use of node ranking to elect cluster heads, contrary to other existing cluster-based work that selects a random node or the node with the highest energy at a particular time instance as the new cluster head. Moreover, a global knowledge strategy is used to maintain a level of universal awareness of existing nodes in the subject area and to avoid the problem of disconnected or forgotten nodes. In the area of data dissemination, the aim of this research is to effectively manage the data collection by developing an efficient data collection scheme using a ferry node and applying a selective duty cycle strategy to the sensor nodes. Depending on the application, mobile ferries can be used for collecting data in a WSN, especially those that are large in scale, with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry-based approach thus eliminates, or minimizes, the need for the multi-hop forwarding of data, and as a result, energy consumption at the nodes will be significantly reduced. This is especially true for nodes that are near the base station as they are used by other nodes to forward data to the base station. MATLAB is used to design, simulate and evaluate the proposed work against the work that has already been done by others by using various performance criteria
    corecore