Nowadays, routing proposals must deal with a panoply of heterogeneous devices, intermittent connectivity, and the users’ constant need for communication, even in rather challenging networking scenarios. Thus, we propose a Social-aware Content-based Opportunistic Routing Protocol, SCORP, that considers the users’ social interaction and their interests to improve data delivery in urban, dense scenarios. Through simulations, using synthetic mobility and human traces scenarios, we compare the performance of our solution against other two social-aware solutions, dLife and Bubble Rap, and the social-oblivious Spray and Wait, in order to show that the combination of social awareness and content knowledge can be beneficial when disseminating data in challenged networks.
Routing in opportunistic networks must be capable of dealing with occasional contacts, intermittent connectivity, highly mobile nodes, power and storage-constrained devices, and the possible nonexistence of end-to-end paths. In the last couple of years, different social-aware opportunistic routing solutions have emerged @cite_8 trying to exploit the less volatile graph created by social proximity metrics in relation to metrics reflecting the mobility behavior of nodes.
@cite_8
189668218 2012927050 2144447514 2127350146
Since users move around based on social relationships and interests, their movement patterns represent how nodes are socially connected (i.e., nodes with strong social ties, nodes that meet occasionally by sharing the same working environment). This means that social interactions reflect personal relationships (e.g., family, friends, co-workers, and passers-by) that may be translated into statistical contact opportunities within and between social groups over time. Such contact opportunities may be exploited to ensure good data dissemination and retrieval, even in the presence of intermittent connectivity. Thus, in the last years, a new routing trend based on social similarity emerged where social relationships, interests, popularity, and among other social characteristics are used to improve opportunistic routing (i.e., routing able to take advantage on intermittent contacts). In this chapter, the reader will learn about the different approaches related to opportunistic routing, focusing on social-aware approaches, and how such approaches make use of social information derived from opportunistic contacts to improve data forwarding. Additionally, a brief overview on the existing taxonomies for opportunistic routing as well as a new one, based on the new social trend, are provided along with a set of experiments in scenarios based on synthetic mobility models and human traces to show the potential of social-aware solutions. Opportunistic routing is being investigated to enable the proliferation of low-cost wireless applications. A recent trend is looking at social structures, inferred from the social nature of human mobility, to bring messages close to a destination. To have a better picture of social structures, social-based opportunistic routing solutions should consider the dynamism of users' behavior resulting from their daily routines. We address this challenge by presenting dLife, a routing algorithm able to capture the dynamics of the network represented by time-evolving social ties between pair of nodes. Experimental results based on synthetic mobility models and real human traces show that dLife has better delivery probability, latency, and cost than proposals based on social structures. Communication networks are traditionally assumed to be connected. However, emerging wireless applications such as vehicular networks, pocket-switched networks, etc., coupled with volatile links, node mobility, and power outages, will require the network to operate despite frequent disconnections. To this end, opportunistic routing techniques have been proposed, where a node may store-and-carry a message for some time, until a new forwarding opportunity arises. Although a number of such algorithms exist, most focus on relatively homogeneous settings of nodes. However, in many envisioned applications, participating nodes might include handhelds, vehicles, sensors, etc. These various "classesrdquo have diverse characteristics and mobility patterns, and will contribute quite differently to the routing process. In this paper, we address the problem of routing in intermittently connected wireless networks comprising multiple classes of nodes. We show that proposed solutions, which perform well in homogeneous scenarios, are not as competent in this setting. To this end, we propose a class of routing schemes that can identify the nodes of "highest utilityrdquo for routing, improving the delay and delivery ratio by four to five times. Additionally, we propose an analytical framework based on fluid models that can be used to analyze the performance of various opportunistic routing strategies, in heterogeneous settings. Opportunistic routing is a recent technique that achieves high throughput in the face of lossy wireless links. The current opportunistic routing protocol, ExOR, ties the MAC with routing, imposing a strict schedule on routers' access to the medium. Although the scheduler delivers opportunistic gains, it misses some of the inherent features of the 802.11 MAC. For example, it prevents spatial reuse and thus may underutilize the wireless medium. It also eliminates the layering abstraction, making the protocol less amenable to extensions to alternate traffic types such as multicast. This paper presents MORE, a MAC-independent opportunistic routing protocol. MORE randomly mixes packets before forwarding them. This randomness ensures that routers that hear the same transmission do not forward the same packets. Thus, MORE needs no special scheduler to coordinate routers and can run directly on top of 802.11. Experimental results from a 20-node wireless testbed show that MORE's median unicast throughput is 22 higher than ExOR, and the gains rise to 45 over ExOR when there is a chance of spatial reuse. For multicast, MORE's gains increase with the number of destinations, and are 35-200 greater than ExOR.