Utility-based Message Replication for Intermittently Connected Heterogeneous Networks

Communication networks (wired or wireless) have traditionally been assumed to be connected at least most of the time. However, emerging applications such as emergency response, special operations, smart environments, VANETs, etc. coupled with node heterogeneity and volatile links (e.g. due to wireless propagation phenomena and node mobility) will likely change the typical conditions under which networks operate. In fact, in such scenarios, networks may be mostly disconnected, i.e., most of the time, end-to-end paths connecting every node pair do not exist. To cope with frequent, long-lived disconnections, {\em opportunistic routing} techniques have been proposed in which, at every hop, a node decides whether it should either forward and/or store-and-carry a message. As a result, a number of message replicas may be created and routed independently (``spraying''). Most opportunistic routing schemes to-date perform {\em greedy} replication handing over a copy of a message to the first nodes encountered. Yet, in a network with heterogeneous nodes, where some nodes may be much ``better'' relays than others, such greedy schemes waste a lot of message replicas (and thus energy, storage space, etc.) on ``useless'' relays. For this reason, we propose the idea of \emph{utility-based replication}, where some \emph{fitness} or \emph{utility} function is maintained for all nodes in a distributed fashion, and a small budget of message replicas is allocated according to this utility only to the fittest nodes. We describe a number of variations using different utility functions, and show that an improvement of up to 5-6 times in delay can be achieved over greedy algorithms

Efficient and adaptive congestion control for heterogeneous delay-tolerant networks

Detecting and dealing with congestion in delay-tolerant networks (DTNs) is an important and challenging problem. Current DTN forwarding algorithms typically direct traffic towards more central nodes in order to maximise delivery ratios and minimise delays, but as traffic demands increase these nodes may become saturated and unusable. We pro- pose CafRep, an adaptive congestion aware protocol that detects and reacts to congested nodes and congested parts of the network by using implicit hybrid contact and resources congestion heuristics. CafRep exploits localised relative utility based approach to offload the traffic from more to less congested parts of the network, and to replicate at adaptively lower rate in different parts of the network with non-uniform congestion levels. We extensively evaluate our work against benchmark and competitive protocols across a range of metrics over three real connectivity and GPS traces such as Sassy [44], San Francisco Cabs [45] and Infocom 2006 [33]. We show that CafRep performs well, independent of network connectivity and mobility patterns, and consistently outperforms the state-of-the-art DTN forwarding algorithms in the face of increasing rates of congestion. CafRep maintains higher availability and success ratios while keeping low delays, packet loss rates and delivery cost. We test CafRep in the presence of two application scenarios, with fixed rate traffic and with real world Facebook application traffic demands, showing that regardless of the type of traffic CafRep aims to deliver, it reduces congestion and improves forwarding performance

Social-aware Opportunistic Routing Protocol based on User's Interactions and Interests

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 challenging networks

Delay Tolerance in Wireless Networks through Optimal Path Routing Algorithm

AbstractA Delay Tolerant Network (DTN) is a mesh network designed to operate effectively over great distances. DTNs have not custom to vindicate complete track from source to destination most of the time during communication. Existing data routing approaches used in DTNs were based on multi-copy routing. However, these existing methods incur overhead due to exorbitant transmissions and increases seer side processing. Hence there is a necessity to propose an optimal path routing algorithm to overcome the above issues. The optimal path routing reduces the proposition of message dropping and wax the throughput. The design approximate also uses random path generation that can reveal the path that affirms active connection for a longer duration to achieve a desired routing delay. In addition, this system has an effective buffer management mechanism to increase throughput and decrease routing delay. The analysis and as well as the simulation results clearly shows that the optimal path routing algorithm, provides high throughput and low routing delay compared to existing routing approaches

Mobility entropy and message routing in community-structured delay tolerant networks

Many message routing schemes have been proposed in the context of delay tolerant networks (DTN) and intermittently connected mobile networks (ICMN). Those routing schemes are tested on specific environments that involve particular mobility complexity whether they are random-based or soci-ologically organized. We, in this paper, propose community structured environment (CSE) and mobility entropy to dis-cuss the effect of node mobility complexity on message rout-ing performance. We also propose potential-based entropy adaptive routing (PEAR) that adaptively carries messages over the change of mobility entropy. According to our simu-lation, PEAR has achieved high delivery rate on wide range of mobility entropy, while link-state routing has worked well only at small entropy scenarios and controlled replication-based routing only at large entropy environments