Search

Energy Harvesting Wireless Body Area Networks (EH-WBANs) where sensor nodes can harvest energy from their ambient environment are of great potential for low-power medical monitoring systems. The availability of energy in EH-WBANs, however, is challenged by uncertainty of harvested energy sources and limited charging efficiency, and thus the energy saving is still of great importance. To improve energy efficiency, this paper studies the sleep scheduling problem in EH-WBANs, which is of fundamental importance for network lifetime and connectivity while being not systematically addressed. Technically, this problem is proved to be NP-Complete, and thus needs non-trivial efforts to deal with. To that end, we present a series of approximation algorithms with proven performance from perspective of constructing minimum dominating set (DS), where energy saving and energy harvesting techniques are combined to prolong the network lifetime while guaranteeing real-time requirement of EH-WBANs. Specifically, we first propose a centralized algorithm that can construct DS with minimum size and discover the maximum number of DSs. We then design two distributed algorithms independent of prior global knowledge to improve network scalability, namely EEU and Improved EEU respectively. Theoretical analysis and extensive simulations are conducted to confirm the superiority of the proposed sleep scheduling algorithms.

Resource management plays a crucial role in improving sum rate of non-orthogonal multiple access (NOMA) networks. However, the traditional resource management methods have considerable complexity, creating a huge challenge in computational efficiency. To handle this challenge, a resource management method is proposed based on a deep neural network (DNN). The key advantage of the method is that the DNN can perform in almost real-time resource allocation because it requires a very simple operation. In this paper, the resource management problem of the NOMA system adopting imperfect successive interference cancellation (SIC) technology at the receivers is studied, including the power allocation stage and the user scheduling stage. For power allocation stage, the generic fully-connected DNN is trained to approximate the power allocation of interior point method (IPM), which not only greatly improves the computational efficiency but also increases the sum rate of the system. Based on the allocated power, the user scheduling algorithm is performed to further increase the system sum rate. Finally, simulation results verify some performances of the proposed algorithms.

The top-k query has long been an important topic in computer science. Efficient implementation of top-k queries is the key for information searching. In this paper, we develop the Efficient algorithm for the Continuous Historical Top-k (ECHT) extraction, which is a novel algorithm that can effectively process the continuous historical top-k query. A simple top-k extraction algorithm based on aggregation is used for user query processing, and two additional steps on the filter setting by which individual nodes do not have to report all their readings are proposed to further reduce communication cost. To the best of our knowledge, this is the first work for continuous historical top-k query processing in sensor networks, and our simulation results show that our schemes can reduce the total communication cost by up to two orders of magnitude, as compared with the centralized scheme or a straightforward extension from the previous top-k algorithm on a continuous monitoring query.

Wireless sensor-actuator networks (WSANs) have recently been suggested as an extension to conventional sensor networks. The powerful and mobile actuators can patrol along different routes and communicate with the static sensor nodes. Obviously, it is crucial to optimize the routes for the actuators to collect the sensor data in a timely fashion. Given the nonuniform and time-varying distribution of sensors and events in large networks, the route design has to be dynamic and scalable as well as balance the loads of the actuators. In this paper, we propose probabilistic route design (PROUD), which is an effective and adaptive algorithm for weight-differentiated route calculation. PROUD constructs an a priori route that covers the sensor locations, following which, the actuators probabilistically and cyclically visit the sensor locations according to their weights. We show that this probabilistic approach adapts well to network dynamics without frequent recalculation of the whole route. It works for both small-scale sensor-actuator networks and large-scale sensor-actuator networks with partitioning. We further develop a distributed implementation of PROUD and extend it to accommodate actuators with variable speeds. Finally, we devise a multiroute improvement and a task-exchange algorithm that enable load balancing. Our performance evaluation shows that PROUD effectively reduces the overall data-collection time and evenly distributes the energy consumption across the actuators, as compared with other state-of-the-art solutions.

A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, are often aggregated (or compressed). This compression is done by a subset of the sensors called "aggregators." Inasmuch as sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, the authors first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS) protocol. Then, they generalize it to an aggregation hierarchy and extend EPAS to Hierarchical EPAS. The optimal number of aggregators with generalized compression and power-consumption models was derived, and fully distributed algorithms for aggregator selection were presented. Simulation results show that the algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols and are thus applicable to a broad spectrum of application environments