An Optimal Adaptive Network Coding Scheme for Minimizing Decoding Delay in Broadcast Erasure Channels

With the emergence of vehicular Internet-of-Things (IoT) applications, it is a significant challenge for vehicular IoT systems to obtain higher throughput in vehicle-to-cloud multipath transmission. Network Coding (NC) has been recognized as a promising paradigm for improving vehicular wireless network throughput by reducing packet loss in transmission. However, existing researches on NC do not consider the influence of the rapid quality change of wireless links on NC schemes, which poses a great challenge to dynamically adjust the coding rate according to the variation of link quality in vehicle-to-cloud multipath transmission in order to avoid consuming unnecessary bandwidth resources and to increase network throughput. Therefore, we propose an Adaptive Network Coding (ANC) scheme brought by the novel integration of the Hidden Markov Model (HMM) into the NC scheme to efficiently adjust the coding rate according to the estimated packet loss rate (PLR). The ANC scheme conquers the rapid change of wireless link quality to obtain the utmost throughput and reduce the packet loss in transmission. In terms of the throughput performance, the simulations and real experiment results show that the ANC scheme outperforms state-of-the-art NC schemes for vehicular wireless multipath transmission in vehicular IoT systems.

Recently, it has been proven that retransmission based on Network Coding (NC) can greatly improve the performance of wireless networks with broadcast erasure channels. However, network coding also introduces additional delay at the both sending and receiving sides, resulting in the decline of the Quality of user Experience (QoE). In this paper, we consider the users' waiting time and the decoding delay as the performance metrics and then propose MixCode, a NC-based loss recovery method, to improve the delay performance in heterogeneous wireless networks. MixCode incorporates Opportunistic Network Coding (ONC) with Random Linear Network Coding (RLNC), where a NC packet can be decoded by simply XORing operation at some destinations, but by Gaussian elimination at other destinations. Simulation results show that our proposed algorithm can efficiently reduce both the waiting time and the decoding delay in comparison with the existing NC-based schemes.

The minimum energy required to transmit one bit of information through a network characterizes the most economical way to communicate in a network. In this paper, we show that, under a layered model of wireless networks, the minimum energy-per-bit for multicasting in a mobile ad hoc network can be found by a linear program; the minimum energy-per-bit can be attained by performing network coding. Compared with conventional routing solutions, network coding not only allows a potentially lower energy-per-bit to be achieved, but also enables the optimal solution to be found in polynomial time, in sharp contrast with the NP-hardness of constructing the minimum-energy multicast tree as the optimal routing solution. We further show that the minimum energy multicast formulation is equivalent to a cost minimization with linear edge-based pricing, where the edge prices are the energy-per-bits of the corresponding physical broadcast links. This paper also investigates minimum energy multicasting with routing. Due to the linearity of the pricing scheme, the minimum energy-per-bit for routing is achievable by using a single distribution tree. A characterization of the admissible rate region for routing with a single tree is presented. The minimum energy-per-bit for multicasting with routing is found by an integer linear program. We show that the relaxation of this integer linear program, studied earlier in the Steiner tree literature, can now be interpreted as the optimization for minimum energy multicasting with network coding. In short, this paper presents a unifying study of minimum energy multicasting with network coding and routing.

Integer programming and minimax problems are essential tools in solving various problems that arise in data mining and machine learning such as multi-class data classification and feature selection problems. In this paper, we propose a new hybrid harmony search algorithm by combining the harmony search algorithm with the multidirectional search method in order to solve the integer programming and minimax problems. The proposed algorithm is called multidirectional harmony search algorithm (MDHSA). MDHSA starts the search by applying the standard harmony search for numbers of iteration then the best-obtained solution is passing to the multidirectional search method as an intensification process in order to accelerate the search and overcome the slow convergence of the standard harmony search algorithm. The proposed algorithm is balancing between the global exploration of the harmony search algorithm and the deep exploitation of the multidirectional search method. MDHSA algorithm is tested on seven integer programming problems and 15 minimax problems and compared against 12 algorithms for solving integer programming problems and 11 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer programming and minimax problems in reasonable time.

We study joint network-coding and channel-coding design to optimize delay performance. In our systems, network codes (network layer) are on top of channel codes (physical layer). Network codes run in a rateless random method, i.e., source and intermediate nodes randomly produce and transmit codewords until transmission succeeds. Thus, the rateless network codes have block-erasure-correction capability. The physical layer codewords are disturbed by channel noise. For the constraint of finite transmission time (finite block length), transmission errors are inevitable in the physical layer. We show that the physical-layer erasure probability is affected by both layers. Then, there is an interaction between network codes and channel codes, concerning the delay measure of the networks. We model the delay of each generation as the independent, identically distributed random variables. We show tradeoffs between the network layer and the physical layer on the length of network codewords, and on the transmission time of physical-layer codewords. To measure delay performance, we use expected delay and network-layer decoding error probability of a given delay, which are measures for networks without and with strict maximum-delay constraints, respectively. We show how to evaluate these measures for the coded networks with erasure channels. Then, we formulate problems to optimize the delay performance, and solutions are suggested.

This paper investigates the delay minimization problem for instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D enabled systems, users cooperate to recover all their missing packets. The paper proposes a game theoretic framework as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. The session is modeled by self-interested players in a non-cooperative potential game. The utility functions are designed so as increasing individual payoff results in a collective behavior which achieves both a desirable system performance in a shared network environment and the Nash equilibrium. Three games are developed whose first reduces the completion time, the second the maximum decoding delay and the third the sum decoding delay. The paper, further, improves the formulations by including a punishment policy upon collision occurrence so as to achieve the Nash bargaining solution. Learning algorithms are proposed for systems with complete and incomplete information, and for the imperfect feedback scenario. Numerical results suggest that the proposed game-theoretical formulation provides appreciable performance gain against the conventional point-to-multipoint (PMP), especially for reliable user-to-user channels.

Erasure correcting codes are widely used in upper layers of packet-switched networks, where the packet erasures often exhibit bursty patterns. The conventional wisdom to deal with bursty erasures is to apply block interleaving to break down the bursty patterns prior to error correcting coding, or use long-block Reed-Solomon codes. We show that they unnecessarily lead to sub-optimal decoding delay. In this work, with the problem model of multiple erasure bursts present in a coding block, we study the fundamental tradeoff among the rate, decoding delay and burst correction performance of erasure correcting codes. Focusing on a class of codes achieving the Singleton bound, we show that the lowest delay to recover any individual symbol not only depends on the erasure burst patterns correctable by a code, but also on whether the source symbols are encoded causally or non-causally. We also describe a few practical linear code constructions that achieve the performance limit discussed.

In this paper, we propose a new hybrid social spider algorithm with simplex Nelder-Mead method in order to solve integer programming and minimax problems. We call the proposed algorithm a Simplex Social Spider optimization (SSSO) algorithm. In the the proposed SSSO algorithm, we combine the social spider algorithm with its powerful capability of performing exploration, exploitation, and the Nelder-Mead method in order to refine the best obtained solution from the standard social spider algorithm. In order to investigate the general performance of the proposed SSSO algorithm, we test it on 7 integer programming problems and 10 minimax problems and compare against 10 algorithms for solving integer programming problems and 9 algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.

Emerging wireless multicast applications simultaneously impose two requirements to the underlying communication networks: to provide sufficient bandwidth and to support a variety of quality-of-service (QoS) sensitivities. For the first requirement, network coding has been proposed recently as an effective way of improving bandwidth utilization. However, almost all previous works about network coding focus on the throughput gain without considering the QoS requirements. Optimal network code construction in wireless multicast under different QoS constraints remains as a significant challenge. In this research, we study the QoS-driven network coding problem. We use large deviation principle to establish the relationship among source rate, link condition, QoS requirement, and network code. Using this relationship, under given QoS requirements, we solve the optimal network code construction problem. The proposed network code supports maximal source rate without violating the QoS requirements. These results constitute the foundations for future designing and implementing network coding based wireless multicast protocols.

Chapter 1 - An Introduction to Network Coding

This paper considers the interplay between rate adaptation and inter-session network coding gains in wireless mesh or ad hoc networks. Inter-session network coding opportunities at relay nodes depend on packets being overheard by surrounding nodes ? The more packets nodes overhear, the more opportunities relays have to combine packets, resulting in a potential increase in network throughput. Thus, by adapting its transmission rate, a node can increase the range over which its packets are overheard, enabling additional opportunities for coding and increased overall throughput. This paper considers inter-session coding, restricted to a single relay (bottleneck) node, or star network topology. Even for such simple topologies the optimal joint rate adaptation and network coding policy is known to be NP-hard. Optimal rate vector selection is a combinatorial optimization problem, which is NP-hard, and finding optimal coding scheme turns out to be a clique partitioning problem which is also NP-hard. So, we provide heuristics to find a suboptimal rate vector and coding scheme. Additionally, we provide a linear programming formulation for network coding when only pairwise intersession coding is allowed. We evaluate the averaged throughput in two different scenarios, in which relays have different access opportunities, giving some intuition on the impact of rate adaptation in lightly and heavily loaded systems. The gains of joint rate adaptation and network coding are marginal when the relay has a higher access opportunity than other nodes, or when the MAC operates ideally it ranges from 9% to 19% as compared to a network without network coding and 4% over a network using regular network coding. While, when the relay has equal access opportunity as other source nodes, which is more typical of todays MAC protocols under heavy loads, the gain ranges from 40% to 62% as compared to the standard relaying case and is upto around 20% as compared to a network with regular pairwise network coding. This can further be increased to a gain of 40% to 120% by replacing pairwise coding with sub-optimal general network coding scheme.

Multi-Robot Path Planning for Each Robot with Several Jobs in a Single Trip

Digital micro fluidic biochips have become one of the most promising technologies in many biomedical fields. In a digital micro fluidic biochip, a group of (adjacent) cells in the micro fluidic array can be configured to work as storage, functional operations, as well as for transporting fluid droplets dynamically. There has been proposed a very efficient Integer Linear Programming (ILP)-based method to schedule operations with considering this dynamic reconfigurability. However, the method cannot directly deal with a case having an operation with multiple successors, which is necessary in some bioassays, e.g., Sample preparation applications. Sample preparation is a crucial preprocessing step to produce droplets of the desired target concentrations from input reagents. In every optimized application graphs for sample preparation, there are many operations with more than one successors. Thus, the previous method cannot be used to get an optimal scheduling, and thus we propose a new framework to use another ILP formulation for scheduling operations in a digital micro fluidic biochips. Our formulation can successfully deal with a case where there is a node with multiple successors. Also, our method can find a binding solution at the same time unlike the above-mentioned previous one thanks to our new ILP formulation. Although our ILP formulation seems to be a bit more complex than the previous one, a preliminary experiment demonstrates that the proposed ILP formulation can successfully find the optimal scheduling for practical sample preparation applications within a reasonable time.

In high-level synthesis, loop pipelining is a technique to improve the throughput and utilisation of hardware datapaths by starting new loop iterations after a fixed amount of time, called the initiation interval (II), allowing to overlap subsequent iterations. The problem is to find the smallest II and corresponding operation schedule that fulfils all data dependencies and resource constraints, both of which are usually found by modulo scheduling.We propose Moovac1, a novel integer linear program (ILP) formulation of the modulo scheduling problem based on overlap variables to model exact resource constraints. Given enough time, Moovac will find a mimimum-II solution. This is in contrast to Canis' state-of-the-art Modulo SDC approach, which requires heuristic simplifications of the resource constraints. Moovac can thus be used as a reference to evaluate heuristics, or in a time-limited mode as a heuristic itself to provide a best-so-far solution.We schedule kernels from the CHStone and MachSuite benchmarks for loop pipelining with Moovac, Modulo SDC and a prior exact formulation by Eichenberger.Moovac has competitive performance in its time-limited mode, and delivers better results faster than the Modulo SDC scheduler for some loops. Often its structure leads to quicker solution times than Eichenberger's formulation.Using the Moovac-computed optimal solutions as a reference, we can confirm that the Modulo SDC heuristic is indeed capable of finding optimal or near-optimal solutions for the majority of small- to medium-sized loops. However, for larger loops the two algorithms begin to diverge, with Moovac often being significantly faster to prove the infeasibility of a candidate II. This can be exploited by running both schedulers synergistically, leading to a quicker convergence to the final II.

Introducción: Las decisiones operativas de un minorista implican que a través de su surtido se pueda satisfacer la demanda logrando la mayor utilidad posible haciendo el mejor uso posible de su espacio de exhibición. Este artículo presenta un modelo de programación lineal entera mixta cuyo propósito es maximizar la rentabilidad de un minorista considerando los efectos de sustitución dinámica entre artículos, impactos de la elasticidad del espacio sobre la demanda, niveles de servicio esperados de la exhibición y las decisiones de los compradores. Objetivo: Proponer un modelo que soporte la toma de decisiones de asignación de espacio, planeación del surtido y localización en góndola de un minorista. Metodología: Primero se analizan diferentes factores que afectan las decisiones objeto de estudio usando fuentes primarias y secundarias. Luego se desarrolla el modelo matemático y se evalúa su desempeño utilizando datos reales de una categoría de productos en un punto de venta de una cadena de supermercados. Resultados: En la instancia de prueba se obtiene la solución óptima en un tiempo computacional razonable y esta solución permite establecer la forma en la cual gestionar de mejor manera el surtido en el punto de venta, superando los resultados del método actual. Conclusiones: El modelo propuesto permite aumentar la utilidad de la cadena a través del surtido adecuado, con los espacios de exhibición justos y con la localización exacta de los artículos seleccionados.