Practical opportunistic data collection in wireless sensor networks with mobile sinks

Wireless Sensor Networks with Mobile Sinks (WSN-MSs) are considered a viable alternative to the heavy cost of deployment of traditional wireless sensing infrastructures at scale. However, current state-of-the-art approaches perform poorly in practice due to their requirement of mobility prediction and specific assumptions on network topology. In this paper, we focus on lowdelay and high-throughput opportunistic data collection in WSN-MSs with general network topologies and arbitrary numbers of mobile sinks. We first propose a novel routing metric, Contact-Aware ETX (CA-ETX), to estimate the packet transmission delay caused by both packet retransmissions and intermittent connectivity. By implementing CA-ETX in the defacto TinyOS routing standard CTP and the IETF IPv6 routing protocol RPL, we demonstrate that CA-ETX can work seamlessly with ETX. This means that current ETXbased routing protocols for static WSNs can be easily extended to WSN-MSs with minimal modification by using CA-ETX. Further, by combing CA-ETX with the dynamic backpressure routing, we present a throughput-optimal scheme Opportunistic Backpressure Collection (OBC). Both CA-ETX and OBC are lightweight, easy to implement, and require no mobility prediction. Through test-bed experiments and extensive simulations, we show that the proposed schemes significantly outperform current approaches in terms of packet transmission delay, communication overhead, storage overheads, reliability, and scalability

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

A mobile agent and message ferry mechanism based routing for delay tolerant network

Delay Tolerant Network (DTN) is a class of networks characterized by long delays, frequent disconnections and partitioning of communication paths between network nodes. Due to the frequent disconnection and network partitioning, the overall performance of the network will be deteriorated sharply. The problem is how to make the network fairly connected to optimize data routing and enhance the performance of a network. The aim of this study is to improve the performance of DTN by minimizing end-to-end delivery time and increasing message delivery ratio. Therefore, this research tackles the problem of intermittent connectivity and network partitioning by introducing Agents and Ferry Mechanism based Routing (AFMR). The AFMR comprises of two stages by applying two schemes: mobile agents and ferry mechanism. The agents' scheme is proposed to deal with intermittent connectivity and network partitioning by collecting the basic information about network connection such as signal strength, nodes position in the network and distance to the destination nodes to minimize end-to-end delivery time. The second stage is to increase the message delivery ratio by moving the nodes towards the path with available network connectivity based on agents' feedback. The AFMR is evaluated through simulations and the results are compared with those of Epidemic, PRoPHET and Message Ferry (MF). The findings demonstrate that AFMR is superior to all three, with respect to the average end-to-end delivery time, message delivery ratio, network load and message drop ratio, which are regarded as extremely important metrics for the evaluation of DTN routing protocols. The AFMR achieves improved network performance in terms of end-to-end delivery time (56.3%); enhanced message delivery ratio (60.0%); mitigation of message drop (63.5%) and reduced network load (26.1 %). The contributions of this thesis are to enhance the performance of DTN by significantly overcoming the intermittent connectivity and network partitioning problems in the network

Interest aware peoplerank: towards effective social-based opportunistic advertising

Various emerging context aware social-based applications and services assume constant non-disruptive connectivity. Mobile advertisers in such environments want to reach potentially interested users in a given proximity and within a specified short duration, whether these users are connected to the network or not. While opportunistic forwarding algorithms can be leveraged for forwarding these advertisements, there is little incentive for those not interested in the ad to act as forwarders. Our goal in this paper is to leverage explicit interest, gathered from a user’s social profile, and integrate it with social-based opportunistic forwarding algorithms in order to enable soft real time opportunistic ad delivery in intermittently connected mobile networks. We propose IPeR, a fully distributed interest-aware forwarding algorithm that integrates with PeopleRank to reduce the overall cost and delay while reducing the number of contacted uninterested candidates. Our results, obtained via simulations and validated with real mobility traces coupled with user social data, are promising. In comparison to interest-oblivious socially-aware protocols such as PeopleRank, the IPeR approach reduces the cost to 70% to reach the same delivery ratio, and reduces the ratio of contacted uninterested forwarders by 23%. It also achieves an extra 70% recall and 107% accuracy with only 2% less precision

Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces