Optimal scheduling and fair servicepolicy for STDMA in underwater networks with acoustic communications

In this work, a multi-hop string network with a single sink node is analyzed. A periodic optimal scheduling for TDMA operation that considers the characteristic long propagation delay of the underwater acoustic channel is presented. This planning of transmissions is obtained with the help of a new geometrical method based on a 2D lattice in the space-time domain. In order to evaluate the performance of this optimal scheduling, two service policies have been compared: FIFO and Round-Robin. Simulation results, including achievable throughput, packet delay, and queue length, are shown. The network fairness has also been quantified with the Gini index

Optimal Cell Clustering and Activation for Energy Saving in Load-Coupled Wireless Networks

Optimizing activation and deactivation of base station transmissions provides an instrument for improving energy efficiency in cellular networks. In this paper, we study optimal cell clustering and scheduling of activation duration for each cluster, with the objective of minimizing the sum energy, subject to a time constraint of delivering the users' traffic demand. The cells within a cluster are simultaneously in transmission and napping modes, with cluster activation and deactivation, respectively. Our optimization framework accounts for the coupling relation among cells due to the mutual interference. Thus, the users' achievable rates in a cell depend on the cluster composition. On the theoretical side, we provide mathematical formulation and structural characterization for the energy-efficient cell clustering and scheduling optimization problem, and prove its NP hardness. On the algorithmic side, we first show how column generation facilitates problem solving, and then present our notion of local enumeration as a flexible and effective means for dealing with the trade-off between optimality and the combinatorial nature of cluster formation, as well as for the purpose of gauging the deviation from optimality. Numerical results demonstrate that our solutions achieve more than 60% energy saving over existing schemes, and that the solutions we obtain are within a few percent of deviation from global optimum.Comment: Revision, IEEE Transactions on Wireless Communication

Modified Greedy Physical Link Scheduling Algorithm for Improving Wireless Mesh Network Performance

The algorithm to allocate mesh active link to radio resource timeslot in wireless mesh network (WMN) is investigated. This paper proposes the novel method to allocate multiple links in one timeslot for improving the wireless mesh network throughput via spatial time division multiple access (STDMA) protocol. The throughput improvement is obtained by modifying greedy based algorithm that is widely known as a low complexity algorithm. We propose and investigate new parameters in the greedy based algorithm that can be used as scheduling control parameters, i.e. interference weight, scheduling weight, and the sum of link’s degree. Simulation results indicate that this approximation increases network performance in throughput and length of scheduling performance closed to the upper bound performance that is achieved by the algorithm that uses the physical interference model.

An Analytical Model for Wireless Mesh Networks with Collision-Free TDMA and Finite Queues

Wireless mesh networks are a promising technology for connecting sensors and actuators with high flexibility and low investment costs. In industrial applications, however, reliability is essential. Therefore, two time-slotted medium access methods, DSME and TSCH, were added to the IEEE 802.15.4 standard. They allow collision-free communication in multi-hop networks and provide channel hopping for mitigating external interferences. The slot schedule used in these networks is of high importance for the network performance. This paper supports the development of efficient schedules by providing an analytical model for the assessment of such schedules, focused on TSCH. A Markov chain model for the finite queue on every node is introduced that takes the slot distribution into account. The models of all nodes are interconnected to calculate network metrics such as packet delivery ratio, end-to-end delay and throughput. An evaluation compares the model with a simulation of the Orchestra schedule. The model is applied to Orchestra as well as to two simple distributed scheduling algorithms to demonstrate the importance of traffic-awareness for achieving high throughput.Comment: 17 pages, 14 figure

A dynamic distributed multi-channel TDMA slot management protocol for ad hoc networks

With the emergence of new technologies and standards for wireless communications and an increase in application and user requirements, the number and density of deployed wireless ad hoc networks is increasing. For deterministic ad hoc networks, Time-Division Multiple Access (TDMA) is a popular medium access scheme, with many distributed TDMA scheduling algorithms being proposed. However, with increasing traffic demands and the number of wireless devices, proposed protocols are facing scalability issues. Besides, these protocols are achieving suboptimal spatial spectrum reuse as a result of the unsolved exposed node problem. Due to a shortage of available spectrum, a shift from fixed spectrum allocation to more dynamic spectrum sharing is anticipated. For dynamic spectrum sharing, improved distributed scheduling protocols are needed to increase spectral efficiency and support the coexistence of multiple co-located networks. Hence, in this paper, we propose a dynamic distributed multi-channel TDMA (DDMC-TDMA) slot management protocol based on control messages exchanged between one-hop network neighbors and execution of slot allocation and removal procedures between sender and receiver nodes. DDMC-TDMA is a topology-agnostic slot management protocol suitable for large-scale and high-density ad hoc networks. The performance of DDMC-TDMA has been evaluated for various topologies and scenarios in the ns-3 simulator. Simulation results indicate that DDMC-TDMA offers near-optimal spectrum utilization by solving both hidden and exposed node problems. Moreover, it proves to be a highly scalable protocol, showing no performance degradation for large-scale and high-density networks and achieving coexistence with unknown wireless networks operating in the same wireless domain

CROSS-LAYER SCHEDULING PROTOCOLS FOR MOBILE AD HOC NETWORKS USING ADAPTIVE DIRECT-SEQUENCE SPREAD-SPECTRUM MODULATION

We investigate strategies to improve the performance of transmission schedules for mobile ad hoc networks (MANETs) employing adaptive direct-sequence spread-spectrum (DSSS) modulation. Previously, scheduling protocols for MANETs have been designed under the assumption of an idealized, narrowband wireless channel. These protocols perform poorly when the channel model incorporates distance-based path loss and co-channel interference. Wideband communication systems, such as DSSS systems, are more robust in the presence of co-channel interference; however, DSSS also provides multiple-access capability that cannot be properly leveraged with a protocol designed for narrowband systems. We present a new transmission scheduling protocol that incorporates link characteristics, spreading factor adaptation, and packet capture capability into scheduling and routing decisions. This provides greater spatial reuse of the channel and better adaptability in mobile environments. Simulation results demonstrate the merits of this approach in terms of end-to-end packet throughput, delay, and completion rate for unicast traffic. We also discuss two variations of the protocol: one provides a method for enhancing the network topology through exchange of local information, and the other leverages multi-packet reception (MPR) capability to enhance the network topology. We show that each approach is useful in networks with sparse connectivity. We conclude by studying the capacity of the networks used in previous sections, providing insight on methods for realizing further performance gains

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