TDMA scheduling algorithms for wireless sensor networks

Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or level-based scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10–70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.     

Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks

This paper presents an efficient scheme to optimize multiple layers in multi-hop wireless networks with throughput objectives. Considering channel sensing and power control at the physical layer, a non-convex throughput optimization problem is formulated for resource allocation and a genetic algorithm is designed to allow distributed implementation. To address link and network layers, a localized back-pressure algorithm is designed to make routing, scheduling, and frequency band assignments along with physical-layer considerations. Our multi-layer scheme is extended to cognitive radio networks with different user classes and evaluate our analytical solution via simulations. Hardware-in-the-loop emulation test results obtained with real radio transmissions over emulated channels are presented to verify the performance of our distributed multi-layer optimization solution for multi-hop wireless networks. Finally, a security system is considered, where links have their security levels and data flows require certain security levels on each of its links. This problem is addressed by formulating additional constraints to the optimization problem.     

Lifetime Maximization Based on Coverage and Connectivity in Wireless Sensor Networks

We consider information retrieval in a wireless sensor network deployed to monitor a spatially correlated random field. We address optimal sensor scheduling and information routing under the performance measure of network lifetime. Both single-hop and multi-hop transmissions from sensors to an access point are considered. For both cases, we formulate the problems as integer programming based on the theories of coverage and connectivity in sensor networks. We derive upper bounds for the network lifetime that provide performance benchmarks for suboptimal solutions. Suboptimal sensor scheduling and data routing algorithms are proposed to approach the lifetime upper bounds with reduced complexity. In the proposed algorithms, we consider the impact of both the network geometry and the energy consumption in communications and relaying on the network lifetime. Simulation examples are used to demonstrate the performance of the proposed algorithms as compared to the lifetime upper bounds.     

Cross‐Layer Resource Allocation in Multi‐interface Multi‐channel Wireless Multi‐hop Networks

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi‐channel wireless multi‐hop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems — flow control; next‐hop routing; rate allocation and scheduling; power control; and channel allocation — and finally solved by a low‐complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.     

Characterizing Achievable Rates in Multi-Hop Wireless Mesh Networks With Orthogonal Channels

This paper considers the problem of determining the achievable rates in multi-hop wireless mesh networks with orthogonal channels. We classify wireless networks with orthogonal channels into two types, half duplex and full duplex, and consider the problem of jointly routing the flows and scheduling transmissions to achieve a given rate vector. We develop tight necessary and sufficient conditions for the achievability of the rate vector. We develop efficient and easy to implement Fully Polynomial Time Approximation Schemes for solving the routing problem. The scheduling problem is a solved as a graph edge-coloring problem. We show that this approach guarantees that the solution obtained is within 50% of the optimal solution in the worst case (within 67% of the optimal solution in a common special case) and, in practice, is close to 90% of the optimal solution on the average. The approach that we use is quite flexible and can be extended to handle more sophisticated interference conditions, and routing with diversity requirements.     

Maximum bandwidth routing and maximum flow routing in wireless mesh networks

The emergence of nomadic multimedia applications, such as multimedia conferencing, distance learning, video phones, video/movie-on-demand, and education-on-demand, has recently generated much interest in multi-hop wireless mesh networks (WMNs) to support diverse Quality-of-Service (QoS). In the existing WMN QoS routing protocols, the methods of bandwidth calculation and allocation were proposed to determine routes with bandwidth guaranteed for QoS applications. This paper studies two NP-hard problems, the maximum bandwidth routing problem (abbreviated to MBRP) and the maximum flow routing problem (abbreviated to MFRP). Given a source node s and a destination node d in a multi-hop wireless mesh network, the MBRP is to determine an s-to-d path that can carry a maximum amount of traffic from s to d and the MFRP is to determine the maximum flow from s to d, both retaining the network bandwidth-satisfied. In this paper, heuristic algorithms for the two problems are proposed. Upper bounds on their optimal values are derived, and a lower bound is derived on the feasible value obtained for the MBRP. With the upper bound and the lower bound, an approximation ratio for the heuristic algorithm of the MBRP is obtained. The effectiveness of the heuristic algorithms is further verified by experiments. A generalized interference model is also discussed.     

Urgency-based packet scheduling and routing algorithms for delay-sensitive data over MANETs

This paper proposes urgency-based packet scheduling and routing algorithms to effectively deliver delay-sensitive data over a multi-hop mobile ad hoc networks supporting IEEE 802.11 multi-rate service. First, packet urgency, node urgency, and route urgency are defined on the basis of the end-to-end delay requirement. Based on these urgency metrics and the estimated transmission delay of each packet by Kalman filter, the proposed packet scheduling algorithm determines the transmission order and drop policy to minimize the node urgency without unnecessary packet drop, and the proposed routing algorithm establishes a route to minimize the derivative of route urgency in order to maximize the number of packets delivered within the required end-to-end delay. Finally, experimental results are presented to evaluate the performance of the proposed joint working algorithms.     

Performance of Random Access Scheduling Schemes in Multi-Hop Wireless Networks

The scheduling problem in multi-hop wireless networks has been extensively investigated. Although throughput optimal scheduling solutions have been developed in the literature, they are unsuitable for multi-hop wireless systems because they are usually centralized and have very high complexity. In this paper, we develop a random-access based scheduling scheme that utilizes local information. The important features of this scheme include constant-time complexity, distributed operations, and a provable performance guarantee. Analytical results show that it guarantees a larger fraction of the optimal throughput performance than the state-of-the-art. Through simulations with both single-hop and multi-hop traffics, we observe that the scheme provides high throughput, close to that of a well-known highly efficient centralized greedy solution called the greedy maximal scheduler.     

Optimal backpressure routing for wireless networks with multi-receiver diversity

We consider the problem of optimal scheduling and routing in an ad-hoc wireless network with multiple traffic streams and time varying channel reliability. Each packet transmission can be overheard by a subset of receiver nodes, with a transmission success probability that may vary from receiver to receiver and may also vary with time. We develop a simple backpressure routing algorithm that maximizes network throughput and expends an average power that can be pushed arbitrarily close to the minimum average power required for network stability, with a corresponding tradeoff in network delay. When channels are orthogonal, the algorithm can be implemented in a distributed manner using only local link error probability information, and supports a “blind transmission” mode (where error probabilities are not required) in special cases when the power metric is neglected and when there is only a single destination for all traffic streams. For networks with general inter-channel interference, we present a distributed algorithm with constant-factor optimality guarantees.     

Cross-Layer Optimization of Multichannel Multiantenna WMNs

Use of multiple channels can significantly improve the throughput of wireless mesh networks (WMNs). Additionally, recent advances in radio technology have made it possible to realize software-defined radio (SDR), which is capable of switching from one channel to another dynamically. On the other hand, equipping wireless nodes with multiple antennas creates great potential for throughput improvement via interference suppression, spatial multiplexing, and spatial division multiple access techniques. In this paper, we investigate the joint optimization of routing and scheduling in multichannel WMNs, where nodes are equipped with a single SDR and multiple antenna elements. We analyze achievable throughput of these networks under four different multiantenna modes: single user single stream, single user multi stream, multi user single stream, and multi user multi stream, each mode integrates different combinations of multiantenna techniques. We mathematically model scheduling and interference constraints and formulate joint routing and scheduling optimization problem with the objective of maximizing the throughput by minimizing network schedule time such that traffic demands for a set of sessions are satisfied. A column generation-based decomposition approach is proposed to solve the problem. Simulation results are presented to evaluate the impact of number of antennas, number of channels, and number of sessions on the schedule time for the four proposed modes.     

基于塔式格型矢量量化的图像多描述编码算法

多描述编码(MDC)是解决差错信道上图像通信数据包丢失问题的一种新方法，它通过将图像分解为多个独立而又具有一定相关性的描述，并通过不同的信道进行传输，来改善数据丢失条件下的图像解码质量。本文提出了一种图像信号的多描述塔式格型矢量量化编码算法(MDPLVQ)，利用小波树之间的独立性，采用不同的塔式格型矢量量化缩放因子对小波系数进行量化。该算法设计简单，对冗余度的控制容易，实验结果说明了其有效性，其编码压缩性能优于多描述标量量化(MDSQ)、多描述对变换(MDPCT)和多描述零又树(MDEZW)等方法。     

Multi-factor and Distributed Clustering Routing Protocol in Wireless Sensor Networks

One of important issues in wireless sensor networks is how to effectively use the limited node energy to prolong the lifetime of the networks. Clustering is a promising approach in wireless sensor networks, which can increase the network lifetime and scalability. However, in existing clustering algorithms, too heavy burden of cluster heads may lead to rapid death of the sensor nodes. The location of function nodes and the number of the neighbor nodes are also not carefully considered during clustering. In this paper, a multi-factor and distributed clustering routing protocol MFDCRP based on communication nodes is proposed by combining cluster-based routing protocol and multi-hop transmission. Communication nodes are introduced to relay the multi-hop transmission and elect cluster heads in order to ease the overload of cluster heads. The protocol optimizes the election of cluster nodes by combining various factors such as the residual energy of nodes, the distance between cluster heads and the base station, and the number of the neighbor nodes. The local optimal path construction algorithm for multi-hop transmission is also improved. Simulation results show that MFDCRP can effectively save the energy of sensor nodes, balance the network energy distribution, and greatly prolong the network lifetime, compared with the existing protocols.     

A distributed utility‐based scheduling for peer‐to‐peer video streaming over wireless networks

Interactive multimedia applications such as peer‐to‐peer (P2P) video services over the Internet have gained increasing popularity during the past few years. However, the adopted Internet‐based P2P overlay network architecture hides the underlying network topology, assuming that channel quality is always in perfect condition. Because of the time‐varying nature of wireless channels, this hardly meets the user‐perceived video quality requirement when used in wireless environments. Considering the tightly coupled relationship between P2P overlay networks and the underlying networks, we propose a distributed utility‐based scheduling algorithm on the basis of a quality‐driven cross‐layer design framework to jointly optimize the parameters of different network layers to achieve highly improved video quality for P2P video streaming services in wireless networks. In this paper, the quality‐driven P2P scheduling algorithm is formulated into a distributed utility‐based distortion‐delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Specifically, encoding behaviors, network congestion, Automatic Repeat Request/Query (ARQ), and modulation and coding are jointly considered. Then, we provide the algorithmic solution to the formulated problem. The distributed optimization running on each peer node adopted in the proposed scheduling algorithm greatly reduces the computational intensity. Extensive experimental results also demonstrate 4–14 dB quality enhancement in terms of peak signal‐to‐noise ratio by using the proposed scheduling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.