Self-adapting maximum flow routing for autonomous wireless sensor networks

Autonomous wireless sensor networks are subject to power, bandwidth, and resource limitations that can be represented as capacity constraints imposed to their equivalent flow networks. The maximum sustainable workload (i.e., the maximum data flow from the sensor nodes to the collection point which is compatible with the capacity constraints) is the maxflow of the flow network. Although a large number of energy-aware routing algorithms for ad-hoc networks have been proposed, they usually aim at maximizing the lifetime of the network rather than the steady-state sustainability of the workload. Energy harvesting techniques, providing renewable supply to sensor nodes, prompt for a paradigm shift from energy-constrained lifetime optimization to power-constrained workload optimization.     

Dynamic Quality of Service Model for Improving Performance of Multimedia Real-Time Transmission in Industrial Networks

Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows. In particular, video compressors make variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by classical real-time protocols, severely reducing the efficiency of network utilization. Thus, it is necessary to enlarge the communication bandwidth to transfer the compressed multimedia streams using Flexible Time Triggered- Enhanced Switched Ethernet (FTT-ESE) protocol. FTT-ESE provides automation to calculate the compression level and change the bandwidth of the stream. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. This proposed framework deals with a dynamic QoS for multimedia transmission over Ethernet with FTT-ESE protocol. This paper also presents distinct QoS metrics based both on the image quality and network features. Some experiments with recorded and live video streams show the advantages of the proposed framework. To validate the solution we have designed and implemented a simulator based on the Matlab/Simulink, which is a tool to evaluate different network architecture using Simulink blocks.     

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad-hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata-based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster-head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobility-based clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.     

Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary algorithm

This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search for energy-efficient OLSR configurations by using a parallel evolutionary algorithm. The experimental analysis demonstrates that significant improvements over the standard configuration can be attained in terms of power consumption, with no noteworthy loss in the QoS.     

Spine routing in ad hoc networks

An ad hoc network is a multihop wireless network in which mobile hosts communicate without the support of a wired backbone for routing messages. We introduce a self organizing network structure called a spine and propose a spine-based routing infrastructure for routing in ad hoc networks. We propose two spine routing algorithms: (a) Optimal Spine Routing (OSR), which uses full and up-to-date knowledge of the network topology, and (b) Partial-knowledge Spine Routing (PSR), which uses partial knowledge of the network topology. We analyze the two algorithms and identify the optimality-overhead trade-offs involved in these algorithms. This revised version was published online in August 2006 with corrections to the Cover Date.     

A Synchronous Co-Allocation Mechanism for Grid Computing Systems

Grid computing systems are emerging as a computing infrastructure that will enable the use of wide-area network computing systems for a variety of challenging applications. One of these is the ever increasing demand for multimedia from users engaging in a wide range of activities such as scientific research, education, commerce, and entertainment. To provide an adequate level of service to multimedia applications, it is often necessary to simultaneously allocate resources including predetermined capacities from interconnecting networks to the applications. The simultaneous allocation of resources is often referred to as co-allocation in the Grid literature. In this paper, we formally define the co-allocation problem and propose a novel scheme called synchronous queuing (SQ) for implementing co-allocation with quality of service (QoS) assurances in Grids. Unlike existing approaches, SQ does not require advance reservation capabilities at the resources. This enables an SQ-based approach to over subscribe the resources and hence improve resource utilization. The simulation studies performed to evaluate SQ indicate that it outperforms an QoS-based scheme with strict admission control by a significant margin.     

Location Selection for Active Services

Active services are application-specified programs that are executed inside the network. The location where the active service is executed plays an important role. The dynamic behavior of networks requires that the selection of the most suitable location to instantiate a service is done at run time. To dynamically place an active service, information about the network (topology, bandwidth) and the application (type of the service) is necessary. This paper describes a method to dynamically search for available active service locations in the Internet. To be deployed in the current Internet, a solution is required to scale well to large networks, and to demand as little changes to the Internet as possible, especially not at lower network layers. Finally, the solution must be flexible and customizable to take application requirements into account. The proposed solution makes use of the routing path between two end systems. Active service locations that are located close to the routing path are then found via DNS queries. The evaluation shows that the application pays an overhead at start up time. For applications that can tolerate a start up delay, we show with three experiments using a video and an image application that the quality of the application can be increased by a dynamic placement of active services.     

Ant colony optimization for multicasting in static wireless ad-hoc networks

Nodes of wireless ad-hoc networks are generally equipped with batteries. This makes energy a scarce resource. Therefore, power consumption of network operations is critical and subject to optimization. One of the fundamental problems in ad-hoc networks is multicasting. In this work, we consider the so-called minimum energy multicast (MEM) problem in static ad-hoc networks. This problem can be stated as a combinatorial optimization problem. We develop an ant colony optimization algorithm for networks with omni-directional as well as directional antennas. The results show that our algorithm consistently outperforms existing techniques. This work was supported by grant TIN2007-66523 (FORMALISM) of the Spanish Government, and by the EU project FRONTS (FP7-ICT-2007-1) funded by the European Commission under the FET Proactive Initiative Pervasive Adaptation. In addition, Christian Blum acknowledges support from the Ramón y Cajal program of the Spanish Ministry of Science and Innovation, and Hugo Hernández acknowledges support from the Catalan Government through an FI grant.     

On an improved clustering algorithm based on node density for WSN routing protocol

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption.

     

Slime mold inspired routing protocols for wireless sensor networks

Many biological systems are composed of unreliable components which self-organize effectively into systems that achieve a balance between efficiency and robustness. One such example is the true slime mold Physarum polycephalum which is an amoeba-like organism that seeks and connects food sources and efficiently distributes nutrients throughout its cell body. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes with varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we exploit two different mechanisms of the slime mold??s tubular network formation process via laboratory experiments and mathematical behavior modeling to design two corresponding localized routing protocols for wireless sensor networks (WSNs) that take both efficiency and robustness into account. In the first mechanism of path growth, slime mold explores its immediate surroundings to discover and connect new food sources during its growth cycle. We adapt this mechanism for a path growth routing protocol by treating data sources and sinks as singular potentials to establish routes from the sinks to all the data sources. The second mechanism of path evolution is the temporal evolution of existing tubes through nonlinear feedback in order to distribute nutrients efficiently throughout the organism. Specifically, the diameters of tubes carrying large fluxes of nutrients grow to expand their capacities, and tubes that are not used decline and disappear entirely. We adapt the tube dynamics of the slime mold for a path evolution routing protocol. In our protocol, we identify one key adaptation parameter to adjust the tradeoff between efficiency and robustness of network routes. Through extensive realistic network simulations and ideal closed form or numerical computations, we validate the effectiveness of both protocols, as well as the efficiency and robustness of the resulting network connectivity.     

Dynamic Social Community Detection and Its Applications

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.     

A general mathematical performance model for wormhole-switched irregular networks

Irregular topologies are desirable network structures for building scalable cluster systems and very recently they have also been employed in SoC (system-on-chip) design. Many analytical models have been proposed in the literature to evaluate the performance of networks with different topologies such as hypercube, torus, mesh, hypermesh, Cartesian product networks, star graph, and k-ary n-cube; however, to the best of our knowledge, no mathematical model has been presented for irregular networks. Therefore, as an effort to fill this gap, this paper presents a comprehensive mathematical model for fully adaptive routing in wormhole-switched irregular networks. Moreover, since our approach holds no assumption for the network topology, the proposed analytical model covers all the aforementioned models (i.e. it covers both regular and irregular topologies). Furthermore, the model makes no preliminary assumption about the deadlock-free routing algorithm applied to the network. Finally, besides the generality of the model regarding the topology and routing algorithm, our analysis shows that the analytical model exhibits high accuracy which enables it to be used for almost all topologies with all traffic loads.     

A Novel Utility Function for Energy-Efficient Power Control Game in Cognitive Radio Networks

Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.     

Adaptive resource utilization and remote access capabilities in high‐performance distributed systems: The Open HPC++ approach

Development of high-performance distributed applications, called metaapplications, is extremely challenging because of their complex runtime environment coupled with their requirements of high-performance and Quality of Service (QoS). Such applications typically run on a set of heterogeneous machines with dynamically varying loads, connected by heterogeneous networks possibly supporting a wide variety of communication protocols. In spite of the size and complexity of such applications, they must provide the high-performance and QoS mandated by their users. In order to achieve the goal of high-performance, they need to adaptively utilize their computational and communication resources. Apart from the requirements of adaptive resource utilization, such applications have a third kind of requirement related to remote access QoS. Different clients, although accessing a single server resource, may have differing QoS requirements from their remote connections. A single server resource may also need to provide different QoS for different clients, depending on various issues such as the amount of trust between the server and a given client. These QoS requirements can be encapsulated under the abstraction of remote access capabilities. Metaapplications need to address all the above three requirements in order to achieve the goal of high-performance and satisfy user expectations of QoS. This paper presents Open HPC++, a programming environment for high-performance applications running in a complex and heterogeneous run-time environment. Open HPC++ provides application level tools and mechanisms to satisfy application requirements of adaptive resource utilization and remote access capabilities. Open HPC++ is designed on the lines of CORBA and uses an Object Request Broker (ORB) to support seamless communication between distributed application components. In order to provide adaptive utilization of communication resources, it uses the principle of open implementation to open up the communication mechanisms of its ORB. By virtue of its open architecture, the ORB supports multiple, possibly custom, communication protocols, along with automatic and user controlled protocol selection at run-time. An extension of the same mechanism is used to support the concept of remote access capabilities. In order to support adaptive utilization of computational resources, Open HPC++ also provides a flexible yet powerful set of load-balancing mechanisms that can be used to implement custom load-balancing strategies. The paper also presents performance evaluations of Open HPC++ adaptivity and load-balancing mechanisms. This revised version was published online in July 2006 with corrections to the Cover Date.     

Energy-aware routing considering load balancing for SDN: a minimum graph-based Ant Colony Optimization

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users’ flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users’ QoS requirements.

     

Finding scalable configurations for AEDB broadcasting protocol using multi-objective evolutionary algorithms

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.     

Modeling,analysis and simulation of ant-based network routing protocols

Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.     

Energy and QoS Aware Routing in Wireless Sensor Networks

Many new routing protocols have been proposed for wireless sensor networks in recent years. Almost all of the routing protocols considered energy efficiency as the ultimate objective since energy is a very scarce resource for sensor nodes. However, the introduction imaging sensors has posed additional challenges. Transmission of imaging data requires both energy and QoS aware routing in order to ensure efficient usage of the sensors and effective access to the gathered measurements. In this paper, we propose an energy-aware QoS routing protocol for sensor networks which can also run efficiently with best-effort traffic. The protocol finds a least-cost, delay-constrained path for real-time data in terms of link cost that captures nodes energy reserve, transmission energy, error rate and other communication parameters. Moreover, the throughput for non-real-time data is maximized by adjusting the service rate for both real-time and non-real-time data at the sensor nodes. Such adjustment of service rate is done by using two different mechanisms. Simulation results have demonstrated the effectiveness of our approach for different metrics with respect to the baseline approach where same link cost function is used without any service differentiation mechanism.Kemal Akkaya received his B.S. degree in Computer Science from Bilkent University, Ankara, Turkey in 1997 and MS degree in Computer Science from Ortadogu Technical University (ODTU), Ankara, Turkey in 1999. He worked as a software developer at an automation project of Siemens and World Bank in Ankara, Turkey in 2000. He is currently a Ph.D. candidate at University of Maryland, Baltimore County (UMBC), Baltimore, MD. His research interests include energy aware routing, security and quality of service issues in ad hoc wireless networks.Mohamed F. Younis received B.S. degree in computer science and M.S. in engineering mathematics from Alexandria University in Egypt in 1987 and 1992, respectively. In 1996, he received his Ph.D. in computer science from New Jersey Institute of Technology. He is currently an assistant professor in the department of computer science and electrical engineering at the university of Maryland Baltimore County (UMBC). Before joining UMBC, he was with the Advanced Systems Technology Group, an Aerospace Electronic Systems R&D organization of Honeywell International Inc. While at Honeywell he led multiple projects for building integrated fault tolerant avionics, in which a novel architecture and an operating system were developed. This new technology has been incorporated by Honeywell in multiple products and has received worldwide recognition by both the research and the engineering communities. He also participated in the development the Redundancy Management System, which is a key component of the Vehicle and Mission Computer for NASAs X-33 space launch vehicle. Dr. Younis technical interest includes network architectures and protocols, embedded systems, fault tolerant computing and distributed real-time systems. Dr. Younis has four granted and three pending patents. He served on multiple technical committees and published over 40 technical papers in refereed conferences and journals.     

Traffic load monitoring and load balancing for the Internet

This paper proposes a class-based multipath routing algorithm to support Quality of Service (QoS). The algorithm is called Two-level Class-based Routing with Prediction (TCRP). Since frequently flooding routing information is very expensive for dynamic routing, the TCRP is designed to have the traffic load information monitored in one stable period as a guide to control traffic forwarding in the next stable period. The monitoring function is implemented by adopting the leaky bucket mechanism. In TCRP, the path selection function can utilize resources on multipath to achieve load balancing, increase network throughput and reduce the queuing delay. The extensive simulation is conducted to analyze the performance of the TCRP algorithm. The simulation results show that the TCRP can reduce packet drops and increase network throughput in any size network topology. This revised version was published online in July 2006 with corrections to the Cover Date.     

Inferring network connectivity by delayed feedback control

We suggest a control based approach to topology estimation of networks with N elements. This method first drives the network to steady states by a delayed feedback control; then performs structural perturbations for shifting the steady states M times; and finally infers the connection topology from the steady states' shifts by matrix inverse algorithm (M = N) or l(1)-norm convex optimization strategy applicable to estimate the topology of sparse networks from M < N perturbations. We discuss as well some aspects important for applications, such as the topology reconstruction quality and error sources, advantages and disadvantages of the suggested method, and the influence of (control) perturbations, inhomegenity, sparsity, coupling functions, and measurement noise. Some examples of networks with Chua's oscillators are presented to illustrate the reliability of the suggested technique.