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Failure of backbone nodes/links can have catastrophic consequences unless they are addressed in a timely manner. Multi-Protocol Label Switching (MPLS), is an increasingly popular technology that employs various recovery mechanisms from such failures. In addition to failure recovery these mechanisms minimize downtime, data loss and convergence time while also avoiding inefficient bandwidth utilization. The aim of this survey paper is to provide an overview of the recovery and bandwidth sharing techniques used by MPLS. We discuss the recovery mechanisms, as well as merits and demerits, of global and local recovery. We also present the bandwidth sharing potential of each recovery technique and discuss different protocols proposed for bandwidth sharing. Our work will be valuable to networking researchers who want to get an insight into the operation mechanism, as well as design tradeoffs, of various MPLS recovery techniques.

Delay Tolerant Networks (DTN) are a class of networks that lack continuous connectivity between nodes due to limited wireless radio coverage, widely scattered mobile nodes, constrained energy resources, high levels of interference or due to some other similar channel impairment. Examples of such networks are those operating in mobile networks or extreme terrestrial environments, or simply as planned networks in space. The term disruption-tolerant network is also occasionally used in place of DTN. Routing in DTN is challenging because of frequent and long duration periods of non-connectivity. Several routing protocols have been proposed with strategies ranging from flooding to opportunistic approaches. Due to the diversity of available strategies, there is a need to properly classify and evaluate their performance with various angles. In this paper, we study state of the art routing protocols and give a comparison between them with respect to the characteristic features and methodology involved. The pros and cons of each, their performance and open research issues are also discussed.

Recently, a variant of the popular wireless mesh network (WMN) technology, in which nodes communicate over multi-hop wireless links, has been proposed to address the problem of wireless interference and the scarcity of wireless spectrum. Such networks, which we shall call cognitive-radio wireless mesh networks (CWMN), can dynamically adapt network parameters according to existing conditions of the wireless channel. Such networks can also spot spectrum holes (or white-spaces, which are licensed spectrum slots currently not being used) and utilize them without causing interference to the incumbent users. There are numerous broadcast-oriented applications in WMNs, and in CWMNs by extension, that are sensitive to end-to-end delay. The inherent susceptibility of wireless links to interference, and the time-varying nature of wireless link's quality, makes the design of efficient broadcasting algorithms for WMNs especially challenging. While minimum-latency broadcasting algorithms have been proposed in literature, they do not take into account the time-varying nature of wireless links in which channels may become unavailable due to interference (or, appearance of an incumbent user). Therefore, using the existing algorithms in CWMNs would necessitate expensive reconstruction of the tree in case of significant change in wireless topology due to interference or appearance of primary user. In this work, we propose a healing algorithm that addresses the problem of time-varying quality of wireless links and avoids expensive reconstruction of the entire tree by fixing the broken tree in a localized and decentralized manner. Simulation results show that our local healing algorithm improves both the broadcast latency and connectivity ratio (in face of interference) as compared to the approach of global reconstruction of trees.

Cognitive radio technology solves the problem of spectrum underutilization by allowing the unlicensed users to opportunistically access available spectrum without affecting the activity of licensed user. Channel assignment and routing in cognitive radio networks is especially challenging in networks where nodes are equipped with only a single transceiver (as is the case in commodity wireless networks that run IEEE 802.11 DCF MAC). We propose a combined framework of routing and channel assignment that exploits channel diversity in cognitive radio networks to optimize routing performance and increase the network capacity. Specifically, we propose a joint cross-layer routing/ channel assignment protocol based on AODV that works without any central control channel and accounts for the state of the links. In this paper, we propose to keep a backup channel to cater for channel heterogeneity thereby avoiding end to end re-route procedures. We also propose cooperative channel switching in which various nodes exchange routing and control information in a coordinated way. Simulation results show that our proposed backup channel approach ensures higher connectivity as compared to the single channel approach as the number of channels interfered with increases.

Internet Protocol Television — IPTV is a new form of traditional TV, in which TV content is delivered using IP based networks which are managed to provide the required level of quality of service, experience, security, interactivity and reliability. Quality of Service (QoS) and Quality of Experience (QoE) are the two major factors which need to be ensured in order to achieve IPTV customer satisfaction. Channel zapping latency is the major problem which affects QoE for IPTV users. In IPTV systems while switching channels, a delay occurs due to different factors like IGMP Join/Leave latency, video steam encoding and decoding time, network jitter and traffic load. These factors hinder the process of IPTV deployment at large scale and pose scalability issues to IPTV. In order to solve IPTV channel zapping latency problem, a number of approaches have been contributed by different researchers but due to bandwidth limitation and video quality issues, every approach has its own tradeoff. In this paper, we survey the current state-of-art in this thread of research and present the insights behind various approaches that have been proposed in literature to solve the problem of increased channel zapping time. In conclusion, we also point out open research questions and future research areas.

We address the problem of minimizing the worst-case broadcast delay in ldquomulti-radio multi-channel multi-rate wireless mesh networksrdquo (MR2-MC WMN) in a distributed and localized fashion. Efficient broadcasting in such networks is especially challenging due to the desirability of exploiting the ldquowireless broadcast advantagerdquo (WBA), the interface-diversity, the channel-diversity and the rate-diversity offered by these networks. We propose a framework that calculates a set of forwarding nodes and transmission rate at these forwarding nodes irrespective of the broadcast source. Thereafter, a forwarding tree is constructed taking into consideration the source of broadcast. Our broadcasting algorithms are distributed and utilize locally available information. We present a detailed performance evaluation of our distributed and localized algorithm and demonstrate that our algorithm can greatly improve broadcast performance by exploiting the rate, interface and channel diversity of MR2-MC WMNs and match the performance of centralized algorithms proposed in literature while utilizing only limited two-hop neighborhood information.

A multi-rate capable IEEE 802.11a/b/g node can utilize different link-layer transmission rates. Interestingly, multi-rate capability is defined by IEEE 802.11 standards only for unicast transmissions. In this paper, we consider a novel type of multiradio multi-channel wireless mesh network (WMN) where a radio can mulicast at different link-layer transmission rates to its neighbors. Such link-layer multi-rate multicast capability will enable low-latency network-layer broadcast/multicast for multimedia. In our previous work, we assumed a "fully multi-rate multicast" (FMM) framework in which nodes can adjust link-layer multicast transmission rate for each link-layer frame. We propose a new framework called "single best-rate multicast" (SBM) that exploits the link-layer rate diversity by enabling each WMN to decide, depending on its topological properties, a single transmission rate for all its link-layer data multicasts. Although, FMM improves performance significantly, employing SBM is attractive since it can eliminate some undesirable features of practical multi-rate Media Access Control (MAC) protocols. We propose methods to determine the "best" link-layer transmission rate for the SBM framework. We also propose two heuristic broadcast solutions, using SBM framework, that can realize low-latency broadcast by exploiting inherent rate and interface diversity in multi-radio multi-channel WMN. Simulation results indicate that SBM broadcast heuristics give comparable performance to FMM broadcast heuristics, especially in dense networks.

We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study the effect of channel assignment on broadcast performance and show that channel assignment can affect the broadcast performance substantially. More importantly, we show that a channel assignment that performs well for unicast does not necessarily perform well for broadcast/multicast. To the best of our knowledge, this work constitutes the first contribution in the area of broadcast routing for MR2-MC WMN

The Internet is inherently a multipath network---for an underlying network with only a single path connecting various nodes would have been debilitatingly fragile. Unfortunately, traditional Internet technologies have been designed around the restrictive assumption of a single working path between a source and a destination. The lack of native multipath support constrains network performance even as the underlying network is richly connected and has redundant multiple paths. Computer networks can exploit the power of multiplicity to unlock the inherent redundancy of the Internet. This opens up a new vista of opportunities promising increased throughput (through concurrent usage of multiple paths) and increased reliability and fault-tolerance (through the use of multiple paths in backup/ redundant arrangements). There are many emerging trends in networking that signify that the Internet's future will be unmistakably multipath, including the use of multipath technology in datacenter computing; multi-interface, multi-channel, and multi-antenna trends in wireless; ubiquity of mobile devices that are multi-homed with heterogeneous access networks; and the development and standardization of multipath transport protocols such as MP-TCP. The aim of this paper is to provide a comprehensive survey of the literature on network-layer multipath solutions. We will present a detailed investigation of two important design issues, namely the control plane problem of how to compute and select the routes, and the data plane problem of how to split the flow on the computed paths. The main contribution of this paper is a systematic articulation of the main design issues in network-layer multipath routing along with a broad-ranging survey of the vast literature on network-layer multipathing. We also highlight open issues and identify directions for future work.

With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.

In recent times, wireless access technology is becoming increasingly commonplace due to the ease of operation and installation of untethered wireless media. The design of wireless networking is challenging due to the highly dynamic environmental condition that makes parameter optimization a complex task. Due to the dynamic, and often unknown, operating conditions, modern wireless networking standards increasingly rely on machine learning and artificial intelligence algorithms. Genetic algorithms (GAs) provide a well-established framework for implementing artificial intelligence tasks such as classification, learning, and optimization. GAs are well known for their remarkable generality and versatility and have been applied in a wide variety of settings in wireless networks. In this paper, we provide a comprehensive survey of the applications of GAs in wireless networks. We provide both an exposition of common GA models and configuration and provide a broad-ranging survey of GA techniques in wireless networks. We also point out open research issues and define potential future work. While various surveys on GAs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on their application in wireless networks.

Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process, and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools can be leveraged to process large amounts of crisis-related data (in the form of user generated data in addition to traditional humanitarian data) to provide an insight into the fast-changing situation and help drive an effective disaster response. This article introduces the history and the future of big crisis data analytics, along with a discussion on its promise, enabling technologies, challenges, and pitfalls.