Graph-based Interference Model Research Articles

Low-complexity scheduling algorithms with constant queue length and throughput guarantees

Distributed scheduling algorithms based on carrier sense multiple access (CSMA) are optimal in terms of the throughput and the steady-state queue lengths. However, they take a prohibitively long time to reach the steady-state, often exponential in the network size. Therefore for large networks that operate over a finite time horizon, apart from the guarantees on the steady-state queue lengths, performance guarantees on the short-term (i.e., transient) queuing behaviour are also required. To that end, we propose distributed scheduling algorithms that are guaranteed to have O(1) expected queue lengths not just in the steady-state but at every time instant, where O(⋅) is with respect to the network size. Further, our algorithms have O(1) complexity and support a constant fraction of the maximum throughput for typical wireless topologies. The central idea of our algorithms is to resolve collisions among pairs of conflicting nodes by assigning a master–follower hierarchy. The master–follower hierarchy can either be chosen randomly or based on the topology of the conflict graph, leading to different performance guarantees.In addition to these hierarchical collision resolution algorithms, which are primarily designed for the conflict graph-based interference model, we also propose an Aloha-based algorithm for the K-neighbour collision tolerance interference model, which is a generalization of the conflict graph model. We show that the proposed Aloha-based algorithm supports a constant fraction of the maximum throughput for typical wireless topologies.

SATPAS: SINR-based adaptive transmission power assignment with scheduling in wireless sensor network

In this work, a centralized metaheuristic algorithm is proposed for assigning minimum transmission power to each sensor node for every transmission link along with a scheduling slot while satisfying the Signal to Interference plus Noise Ratio constraint in the Wireless Sensor Network. Simultaneous communication in all communication links is not possible while maintaining SINR constraints. Thus, the algorithm also tries to maximize the number of concurrent transmission links in each scheduling slot. We formulate the optimization problem with two objective functions as (i) Communication link wise adaptive and minimum transmission power assignment together with scheduling slot number and (ii) Minimization of the required number of scheduling slots. Probabilistic interference model, which is more realistic, is used for calculating the receiver interference, unlike the graph-based interference model mostly described in the literature. Genetic Algorithm with Edge-set and Edge-window-decoder chromosome representation schemes are used for optimization. Extensive simulations are carried out to demonstrate the efficiency of proposed algorithms using different benchmark data sets and special network topologies like spiral, cluster, and mesh. Our proposed algorithm outperforms the existing MST-based algorithm in terms of minimum transmission power requirement, number of scheduling slots required, and rate of convergence.

Distributed Stable Global Broadcasting for SINR-Based Multi-Channel Wireless Multi-Hop Networks

Recently, the problem of Stable Global Broadcasting (SGB) with continuous packet injections at a source node has attracted considerable attention and much work has been carried out. However, existing SGB algorithms are all centralized and under the graph-based interference model. How to achieve efficient distributed SGB under the more realistic Signal-to-Interference-plus-Noise-Ratio (SINR) interference model is still an open issue. In this paper, we focus on the design of distributed SGB algorithms for multi-channel wireless multi-hop networks under the SINR model. We first present an efficient Backbone-based Multi-channel Concurrent Scheduling (BMCS) strategy and prove an upper bound of $1/2$ packets/slot for the broadcast capacity (i.e., the maximum supportable packet injection rate for all possible SGB algorithms). By iterating the BMCS strategy in different ways, we present two distributed SGB algorithms, one for deterministic packet injection model corresponding to the broadcast capacity (called SGB-DPI) and another for stochastic packet injection model (called SGB-SPI). We prove that: 1) both SGB-DPI and SGB-SPI meet the queue stability and latency stability constraints for providing stable global broadcasting services, and 2) SGB-DPI is throughput-optimal. We evaluate the performance of our proposed algorithms through simulations. The simulation results validate our theoretical analysis and also show that even under the stochastic packet injection model, SGB-DPI has a comparable and even better throughput performance than SGB-SPI.

Link scheduling for throughput maximization in multihop wireless networks under physical interference

We consider the problem of link scheduling for throughput maximization in multihop wireless networks. Majority of previous methods are restricted to graph-based interference models. In this paper we study the link scheduling problem using a more realistic physical interference model. Through some key observations about this model, we develop efficient link scheduling algorithms by exploiting the intrinsic connections between the physical interference model and the graph-based interference model. For one variant of the problem where each node can dynamically adjust its transmission power, we design a scheduling method with O(g(E)) approximation to the optimal throughput capacity where g(E) denotes length diversity. For the other variant where each node has a fixed but possible different transmission powers for different nodes, we design a method with O(g(E))-approximation ratio when the transmission powers of all nodes are within a constant factor of each other, and in general with an approximation ratio of $$O(g(E)\log \rho )$$ where $$\log \rho$$ is power diversity. We further prove that our algorithm for fixed transmission power case retains O(g(E)) approximation for any length-monotone, sub-linear fixed power setting. Furthermore, all these approximation factors are independent of network size .

On the Performance of Largest-Deficit-First for Scheduling Real-Time Traffic in Wireless Networks

This paper considers the problem of scheduling real-time traffic in wireless networks. We consider ad hoc wireless networks with general conflict graph-based interference model and single-hop traffic. Each packet is associated with a deadline and will be dropped if it is not transmitted before the deadline. The number of packet arrivals in each time-slot and the maximum delay before the deadline are independent and identically distributed across time. We require a minimum fraction of packets to be delivered. At each link, we assume the link keeps track of the difference between the minimum number of packets that need to be delivered so far and the number of packets that are actually delivered, which we call the deficit. The largest-deficit-first (LDF) policy schedules links in descending order according to their deficit values, which is a variation of the longest-queue-first (LQF) policy for non-real-time traffic. We prove that the efficiency ratio of LDF, which is the fraction of the throughput region that LDF can achieve for given traffic distributions, can be lower-bounded by a quantity that we call the real-time local-pooling factor (R-LPF). We further prove that a lower bound on the R-LPF can be related to the weighted sum of the service rates, with a special case of 1/(β + 1) by considering the uniform weight, where β is the interference degree of the conflict graph. We also propose a heuristic consensus algorithm that can be used to obtain a good weight vector for such lower bounds for given network topology.

Approximation Algorithms for Maximum Link Scheduling under SINR-Based Interference Model

A fundamental problem in wireless networks is the maximum link scheduling (MLS) problem. In this problem, interference is a key issue and past researchers have shown that determining reception using Signal-to-Interference plus Noise Ratio (SINR) is more realistic than graph-based interference models. Unfortunately, the MLS problem has been proven to be NP-hard for SINR interference models. To date, several approximation algorithms have been proposed to solve MLS under the SINR-based interference model. However, most of these works do not have either an approximation bound or a distributed version. To this end, we present a novel scheduling method with a constant approximation ratio which is much simpler and only 1/28 of it in past research. The improvement of constant ϕ also offers a better MLS set. In addition, based on our centralized method, we present a polynomial time, randomized, distributed algorithm, which only requires estimates of the number of links, and maximum and minimum link lengths. We prove its correctness and show that it can compute a MLS with time complexity of [Formula: see text], where n is an estimate of the number of links.

Distributed [formula omitted]-coloring in the physical model

In multi-hop radio networks, such as wireless ad-hoc networks and wireless sensor networks, nodes employ a MAC (Medium Access Control) protocol such as TDMA to coordinate accesses to the shared medium and to avoid interference of close-by transmissions. These protocols can be implemented using standard node coloring. The (Δ+1)-coloring problem is to color all nodes in as few timeslots as possible using at most Δ+1 colors such that any two nodes within distance R are assigned different colors, where R is a given parameter and Δ is the maximum degree of the modeled unit disk graph using R as a scaling factor. Being one of the most fundamental problems in distributed computing, this problem is well studied and there is a long chain of algorithms prescribed for it. However, all previous works are based on abstract models, such as message passing models and graph based interference models, which limit the utility of these algorithms in practice. In this paper, for the first time, we consider the distributed (Δ+1)-coloring problem under the more practical SINR interference model. In particular, without requiring any knowledge about the neighborhood, we propose a novel randomized (Δ+1)-coloring algorithm with time complexity O(Δlog⁡n+log2⁡n). For the case where nodes cannot adjust their transmission power, we give an O(Δlog2⁡n) randomized algorithm, which only incurs a logarithmic multiplicative factor overhead.

Efficient group multicast node scheduling schemes in multi-hop wireless networks

STDMA networks provide an effective solution for enabling wireless devices to access network resources with fairness and efficiency. When multiple-group communications are implemented in such networks, the scheduling algorithm should generate appropriate schedule assignments for all the transmissions where the objective aims to reduce the schedule length. In this paper, the problem of producing an efficient schedule sequence for multiple-group communications over a STDMA node scheduling network is referred to as an integrated multiple-group communication and traffic-oriented node scheduling (IMCTNS) problem. It is shown that the IMCTNS problem can be formulated as an integer linear programming (ILP) problem. A polynomial-time centralized heuristic scheduling algorithm, designated as Broadcasting Level-by-Level Scheduling (B-LBLS), is proposed for determining the schedule sequence subject to transmission constraints. To enhance the spatial utilization efficiency within each time slot, an advanced version of B-LBLS, designated as Collision-Allowed Level-by-Level Scheduling (CA-LBLS), is proposed based on a modified graph-based interference model. It is shown that compared to existing TDMA- and STDMA-based algorithms, the proposed algorithms provide an effective reduction in the schedule frame length and a significant increase in the spatial utilization within each time slot.

MAC Scheduling With Low Overheads by Learning Neighborhood Contention Patterns

Aggregate traffic loads and topology in multihop wireless networks may vary slowly, permitting MAC protocols to “learn” how to spatially coordinate and adapt contention patterns. Such an approach could reduce contention, leading to better throughput. To that end, we propose a family of MAC scheduling algorithms and demonstrate general conditions, which, if satisfied, ensure lattice rate optimality (i.e., achieving any rate-point on a uniform discrete lattice within the throughput region). This general framework enables the design of MAC protocols that meet various objectives and conditions. In this paper, as instances of such a lattice-rate-optimal family, we propose distributed, synchronous contention-based scheduling algorithms that: 1) are lattice-rate-optimal under both the signal-to-interference-plus-noise ratio (SINR)-based and graph-based interference models; 2) do not require node location information; and 3) only require three-stage RTS/CTS message exchanges for contention signaling. Thus, the protocols are amenable to simple implementation and may be robust to network dynamics such as topology and load changes. Finally, we propose a heuristic, which also belongs to the proposed lattice-rate-optimal family of protocols and achieves faster convergence, leading to a better transient throughput.