Efficient Publish/Subscribe-Based Multicast for Opportunistic Networking with Self-Organized Resource Utilization

In the next generation Internet, it is expected that human, smart devices, and “things” will be able to communicate and interact with each other opportunistically in order to share their data. An analysis of this type of relationship is made possible due to the advent of the Opportunistic Internet of Things (OppIoT), a new paradigm that enable information sharing and dissemination among opportunistic communities formed based on human mobility and opportunistic contacts. Designing data dissemination and routing protocols for opportunistic IoT is a challenge since contacts between nodes and users’ social behaviors are to be tighten together as design constraints. Nonetheless, as opportunistic networking rely on spontaneous connectivity between the users and wireless devices, it can be argued that OppIoT is a form of opportunistic social networks (OppNets) extension, with focus on the relationship between human and opportunistic connection of smart things. As such, some routing protocols that have been designed to work for infrastructureless OppNets can also be applied in OppIoT systems. In this context, the History Based Routing Protocol for Opportunistic Networks (HiBOp) is an appealing choice. In this paper, an acknowledgement (ACK) forwarding mechanism to boost the performance of HiBOp is proposed based on the concept of centrality. Simulation results are provided, showing that HiBOp with centrality outperforms HiBOp in terms of predefined performance metrics.

Opportunistic networks are formed by small devices that communicate over a wireless link with each other. Opportunistic network possess various heterogeneous resources. Resource management and scheduling of these heterogeneous resources are major problems in the Opportunistic network due to highly dynamic behavior of the devices in the opportunistic network. Especially challenging is the multisuser aspect in the opportunistic network environment, since with a high and changeable number of users the workload is difficult to predict, and so making a proper scheduling of tasks becomes a serious problem. The overheads imposed by the waiting queues may become a major part of the total execution time. So to efficiently manage the resources in the opportunistic network, a Grid environment is considered to address the problem of fair sharing. An auction model which is an economic based technique has also been considered in the Grid environment. In the proposed work, a second price auction mechanism is considered to employ a pricing mechanism that is incentive compatible. Incentive compatible pricing mechanisms ensure that the users' self-interests are maximized when they honestly reveal their preferences to the allocation mechanism. These mechanisms can provide powerful guarantees about optimal user behavior and can deliver highly efficient allocations. So in the proposed work, a second price auction mechanism is considered which is very efficient and also it elicits honest information from the participants thereby successfully managing the resource allocation in the Grid environment. A Gossip based mechanism is also considered in the proposed work in order to reduce the overheads caused by the waiting queues.

The opportunistic networks are the variants of Delay Tolerant Networks (DTNs). These networks can be useful for routing in places where there are few base stations and connected routes for long distances. In an opportunistic network, when nodes move away or turn off their power to conserve energy, links may be disrupted or shut down periodically. These events result in intermittent connectivity. When there is no path existing between the source and the destination, the network partition occurs. Therefore, nodes need to communicate with each other via opportunistic contacts through store-carry-forward operation. In this work, we consider the IoT device selection problem in opportunistic networks. We propose a delay-aware fuzzy-based system consisting of three input parameters. The output parameter is IoT device selection decision. We evaluate the performance of the proposed system by simulations. The simulation results show that the proposed system makes a proper selection decision of IoT-devices in opportunistic networks.

A geo-temporal mobility prediction method for cooperative time-validity-constrained content delivery in opportunistic networks

Opportunistic networks are a new evolution of mobile ad hoc networks composed of intermittently connected nodes, in which the routing for the dynamic topology is a challenging issue. In opportunistic networks, the mobile nodes with common interest or close relationship may form into groups and move together, which brings us a good feature to employ when designing the routing protocol for opportunistic networks under resource constraints. Our main idea in this paper is to maximize the message delivery probability in a group-aware opportunistic network under the constraints of bandwidth and buffer space. Embedded this idea, we propose a cooperative routing protocol using the group feature, which includes the cooperative message transfer scheme and the buffer management strategy. In the cooperative message transfer scheme, the limited bandwidth is considered and the message transfer priorities are designed to maximize the delivery probability. In the buffer management strategy, by considering the constraint of buffer space, we propose the cooperative message caching scheme and the dropping order of the messages is designed to minimize the reduced delivery probability. Finally, we conduct the simulations to demonstrate the effectiveness of our proposed routing protocol.

Existing routing schemes in opportunistic networks can be classified two categories: multi-copy based schemes and single-copy based schemes. Multi-copy based schemes can increase the robustness of networks and reduce average delay. However, they consume a large amount of network resources and induce large overhead. Single-copy based schemes can save network resources, but it is difficult to guarantee the transmission delay and packet delivery ratio for these schemes. Hence, how to design an efficient routing scheme which can make a tradeoff between multi-copy schemes and single-copy schemes in opportunistic networks with limited resources is a challenging issue. Inspired by this issue, in this paper, we propose a novel routing scheme based on game theory in opportunistic networks under limited resources, named GTR. In GTR, we map the message transmission to a multi-player bargaining model. By constructing proper utility function, GTR can save the network resources, and achieve better performance in dense opportunistic networks. Our extensive simulation results also demonstrate this point, and GTR performs better compared to some classical routing schemes in terms of packet delivery ratio, average delay, overhead ratio and average hops.

This paper describes a multi-level system dynamics (SD) / discrete event simulation (DES) approach for assessing planning and scheduling problems within an aviation training continuum. The aviation training continuum is a complex system, consisting of multiple aviation schools interacting through interschool student and instructor flows that are affected by external triggers such as resource availability and the weather. SD was used to model the overall training continuum at a macro level to ascertain relationships between system entities. SD also assisted in developing a shared understanding of the training continuum, which involves constructing the definitions of the training requirements, resources and policy objectives. An end-to-end model of the continuum is easy to relate to, while dynamic visualisation of system behaviour provides a method for exploration of the model. DES was used for micro level exploration of an individual school within the training continuum to capture the physical aspects of the system including resource capacity requirements, bottlenecks and student waiting times. It was also used to model stochastic events such as weather and student availability. DES has the advantage of being able to represent system variability and accurately reflect the limitations imposed on a system by resource constraints. Through sharing results between the models, we demonstrate a multi-level approach to the analysis of the overall continuum. The SD model provides the school’s targeted demand to the DES model. The detailed DES model is able to assess schedules in the presence of resource constraints and variability and provide the expected capacity of a school to the high level SD model, subjected to constraints such as instructor availability or budgeted number of training systems. The SD model allows stakeholders to assess how policy and planning affect the continuum, both in the short and the long term. The development of this approach permits moving the analysis of the continuum between SD and DES models as appropriate for given system entities, scales and tasks. The resultant model outcomes are propagated between the continuum and the detailed DES model, iteratively generating an assessment of the entire set of plans and schedule across the continuum. Combining data and information between SD and DES models and techniques assures relevance to the stakeholder needs and effective problem scoping and scaling that can also evolve with dynamic architecture and policy requirements. An example case study shows the combined use of the two models and how they are used to evaluate a typical scenario where increased demand is placed on the training continuum. The multi-level approach provides a high level indication of training requirements to the model of the new training school, where the detailed model indicates the resources required to achieve those particular student levels.

With the deep integration of communication technology and Internet of Things technology, the edge network structure is becoming increasingly dense and heterogeneous. At the same time, in the edge network environment, characteristics such as wide-area differentiated services, decentralized deployment of computing and network resources, and highly dynamic network environment lead to the deployment of redundant or insufficient edge cache nodes, which restricts the efficiency of network service caching and resource allocation. In response to the above problems, research on the joint optimization of service caching and resources in the decentralized edge network scenario is carried out. Therefore, we have conducted research on the collaborative caching of training data among multiple edge nodes and optimized the number of collaborative caching nodes. Firstly, we use a multi-queue model to model the collaborative caching process. This model can be used to simulate the in-network cache replacement process on collaborative caching nodes. In this way, we can describe the data flow and storage changes during the caching process more clearly. Secondly, considering the limitation of storage space of edge nodes and the demand for training data within a training epoch, we propose a stochastic gradient descent algorithm to obtain the optimal number of caching nodes. This algorithm entirely takes into account the resource constraints in practical applications and provides an effective way to optimize the number of caching nodes. Finally, the simulation results clearly show that the optimized number of caching nodes can significantly improve the adequacy rate and hit rate of the training data, with the adequacy rate reaching 84% and the hit rate reaching 100%.

Growing Application of Pharmacoeconomics and Outcomes Research in Health-Care Decision-Making in the Asia-Pacific Region

Opportunistic networks are special multi-hop ad hoc networks in which nodes exploit any opportunity of pairwise contact to forward information, without requiring any fixed infrastructures. Routing is one of the most challenging problems in opportunistic networks, because there is not an end-to-end path in most situations. To deal with this issue, researchers have investigated various routing protocols based on redundancy messages or the prediction of future contacts according to the nodes' mobility history. However, most of the previous work ignored the social attributes of nodes, which can be fully utilized to improve routing efficiency. In this paper, we proposes an efficient routing algorithm, community-based routing (CR), where nodes determine which community it belongs to and choose a proper next hop node in order to reduce the number of forwarding and improve the delivery probability. A unique feature of this algorithm is that it records the contact history and calculates the tie strength with other nodes while moving around and exploits the recorded contact history to optimize the forwarding path. We show through simulation that this algorithm is effective and it achieves better performance than Epidemic, Bubble Rap and PROPHET.

To accommodate the dynamic characteristics in opportunistic networks, ``carry-and-forward'' technique is utilized. In this paradigm, the next hop forwarding relay node is usually decided by inferring future contact opportunities. A practical method is to predict future encounter time using nodes' contact history. But limited by prediction accuracy, this method has only an uncertain effect on improving delay performance. To improve the efficiency of selected relay(s), this paper introduces a new metric used to deicide whether an intermediate node is appropriate to be selected. The metric is based on the estimation of Residual Expected Delay (RED), and combined with a threshold value to guard the fidelity of prediction based relay selection judgement. After then, a single-copy and a multi-copy two-hop forwarding algorithms are proposed. Finally, based on the underlying vehicular opportunistic network extracted from large-scale realistic vehicle trace records, we give an empirical evaluation and analysis of the proposed method. Results show the new metric is efficient in improving delivery ratio with a low delivery cost.

Multi-criteria optimization of wireless connectivity over sparse networks

A game theoretic context-based routing protocol for opportunistic networks in an IoT scenario

Maintenance decision-making has emerged as an important area of industrial research. Over the past two decades, maintenance policies have evolved from simple reactive maintenance to complex versions of condition-based maintenance (CBM). A quantitative description of a machine’s health, as found in CBM, is essential to plan maintenance effectively as it helps avoid excessive or insufficient maintenance. In spite of several advancements in the degradation monitoring techniques, most CBM decision-making methods still focus on a single machine system. Maintenance analysis of a single machine provides good insights, but lacks practical applications. In this paper, we develop a continuous time Markov chain degradation model and a cost model to quantify the effects of maintenance on a multiple machine system. An optimal maintenance policy for a multiple machine system in the absence of resource constraints is obtained. In the presence of resource constraints, two prioritization methods are proposed to obtain effective maintenance policies for a multiple machine system. A case study focusing on a section of an automotive assembly line is used to illustrate the effectiveness of the proposed method.

Efficient high level design tools that can map behavioral descriptions to FPGA architectures are one of the key requirements to fully leverage FPGA for high throughput computations and meet time-to-market pressures. We present a compiler that takes as input algorithms described in MATLAB and generates RTL VHDL. The RTL VHDL then can be mapped to FPGAs using existing commercial tools. The input application is mapped to multiple FPGAs by parallelizing the application and embedding communication and synchronization primitives automatically. Our compiler infers the minimum number of bits required to represent the variable through a precision analysis framework. The compiler can leverage optimized IP cores to enhance the hardware generated. The compiler also exploits parallelism in the input algorithm by pipelining in the presence of resource constraints. We demonstrate the utility of the compiler by synthesizing hardware for a couple of signal/image processing algorithms and comparing them with manually designed hardware.