<![CDATA[ IEEE Transactions on Mobile Computing - new TOC ]]>
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TOC Alert for Publication# 7755 2016June 23<![CDATA[A Street-Centric Opportunistic Routing Protocol Based on Link Correlation for Urban VANETs]]>Wiener process to predict the probability of link availability, which considers the stable and unstable vehicle states according to the behavior of vehicles. We introduce a novel concept called the link correlation which represents the influence of different link combinations in network topology to transmit a packet with less network resource consumption and higher goodput. Based on this concept, we design an opportunistic routing metric called the expected transmission cost over a multi-hop path (ETCoP) implemented with our link model as the selection guidance of a relaying node in intra-streets. This metric can also provide assistance for the next street selection at an intersection. Finally, we propose a street-centric opportunistic routing protocol based on ETCoP for VANETs (SRPE). Simulation results show that our proposed SRPE outperforms the conventional protocols in terms of packet delivery ratio, average end-to-end delay, and network yield.]]>157158615991167<![CDATA[A Traffic Adaptive Multi-Channel MAC Protocol with Dynamic Slot Allocation for WSNs]]>157160016133349<![CDATA[ARQ for Physical-Layer Network Coding]]>157161416311119<![CDATA[Compact Conformal Map for Greedy Routing in Wireless Mobile Sensor Networks]]>15716321646947<![CDATA[Detecting Node Failures in Mobile Wireless Networks: A Probabilistic Approach]]>157164716601467<![CDATA[Distributed Workload Dissemination for Makespan Minimization in Disruption Tolerant Networks]]>, which maintains certain neighborhood information at individual nodes. makes dissemination decisions based on the estimations of the potential computational capacities and the future workloads of mobile nodes. Extensive trace-driven simulations confirm the effectiveness of .]]>157166116731162<![CDATA[Enabling Situation Awareness at Intersections for IVC Congestion Control Mechanisms]]>157167416851368<![CDATA[Impact of Duty Cycling on Opportunistic Communication]]>15716861698706<![CDATA[Near-Optimal Velocity Control for Mobile Charging in Wireless Rechargeable Sensor Networks]]> with the proposed velocity control mechanisms.]]>157169917131411<![CDATA[Optimizing Video Request Routing in Mobile Networks with Built-in Content Caching]]>server selection); and (ii) how to route so-generated video flows (i.e., flow routing). In this work, we jointly formulate these two problems with two traffic-engineering objectives considered, namely, minimizing maximum link utilization and minimizing total link cost. We develop fast algorithms to solve the problems with provable approximation guarantees. We then propose a hop-by-hop routing protocol, which implements the optimization solutions by generating a set of flow-splitting and routing decisions for each router/caching node. Simulation results show that our algorithms significantly outperform existing routing schemes under various system settings, reducing up to 68 percent of maximum link utilization and more than 50 percent of link cost, and supporting over 60 percent more of traffic load.]]>15717141727940<![CDATA[Policing 802.11 MAC Misbehaviours]]>157172817421187<![CDATA[Predictive Distributed Visual Analysis for Video in Wireless Sensor Networks]]>157174317561505<![CDATA[Scalable Wireless Traffic Capture Through Community Detection and Trace Similarity]]>157175717691410<![CDATA[SemanticSLAM: Using Environment Landmarks for Unsupervised Indoor Localization]]>SemanticSLAM, a novel unsupervised indoor localization scheme that bypasses the need for war-driving. SemanticSLAM leverages the idea that certain locations in an indoor environment have a unique signature on one or more phone sensors. Climbing stairs, for example, has a distinct pattern on the phone's accelerometer; a specific spot may experience an unusual magnetic interference while another may have a unique set of Wi-Fi access points covering it. SemanticSLAM uses these unique points in the environment as landmarks and combines them with dead-reckoning in a new Simultaneous Localization And Mapping (SLAM) framework to reduce both the localization error and convergence time. In particular, the phone inertial sensors are used to keep track of the user's path, while the observed landmarks are used to compensate for the accumulation of error in a unified probabilistic framework. Evaluation in two testbeds on Android phones shows that the system can achieve meters human median localization errors. In addition, the system can detect the location of landmarks with 0.83 meters median error. This is 62 percent better than a system that does not use SLAM. Moreover, SemanticSLAM has a 33 percent lower convergence time compared to the same systems. This highlights the promise of SemanticSLAM as an unconventional approach for indoor localization.]]>157177017821355<![CDATA[TCP-Aware Backpressure Routing and Scheduling]]>157178317961042<![CDATA[The Minimum <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="dangelo-ieq1-2475765.gif"/></alternatives></inline-formula>-Storage Problem: Complexity, Approximation, and Experimental Analysis]]>storage nodes, which receive raw data from other nodes, compress them, and send them toward a sink. We consider the problem of locating storage nodes in order to minimize the energy consumed for converging the raw data to the storage nodes as well as to converge the compressed data to the sink. This is known as the minimum -storage problem . In general, the problem is -hard. However, we are able to devise a polynomial-time algorithm that optimally solves the problem in bounded-tree width graphs. We then characterize the minimum -storage problem from the approximation viewpoint. We first prove that it is -hard to be approximated within a factor smaller than . We then propose a local search algorithm that guarantees a constant approximation factor. We conducted extended experim-
nts to show that the algorithm performs very well, exhibiting very small deviation from the optimum and computational time. It is worth to note that our problem is a generalization to the well-known metric -median problem and then the obtained results also hold for this case.]]>15717971811570<![CDATA[The Sleepy Bird Catches More Worms: Revisiting Energy Efficient Neighbor Discovery]]>157181218251080<![CDATA[Toward Optimal Distributed Monitoring of Multi-Channel Wireless Networks]]>distributed online solutions for large-scale and dynamic networks. The dynamism in the network may arise from mobility of the nodes being monitored. Our algorithm is guaranteed to achieve at least times the optimum, regardless of the network topology and the channel assignment of nodes to be monitored, while providing a distributed solution amenable to online implementation. Further, our algorithm is cost-effective, in terms of communication and computational overheads, due to the use of purely local communication and the incremental adaptation to network changes. We present two operational modes of our algorithm for two types of networks that change at different rates; one is a proactive mode for fast-varying networks, while the other is a reactive mode for slowly-varying networks. Simulation results demonstrate the effectiveness of the two modes of our algorithm and compare it to the theoretically optimal algorithm.]]>157182618381011<![CDATA[Two-Phase Multicast DRX Scheduling for 3GPP LTE-Advanced Networks]]>3GPP Long Term Evolution-Advanced (LTE-A) is the most promising technology which provides transmission rate up to 1 Gbps and supports various broadband multimedia services, such as IPTV and Voice/Video-over-IP services. To reduce the energy consumption of user equipments (UEs), the LTE-A standard defines the Discontinuous Reception Mechanism (DRX) to allow UEs to turn off their radio interfaces and go to sleep when no data needs to be received. However, how to optimally configure DRX for UEs is still left as an open issue. In this paper, we address the DRX optimization problem for multicast services. This problem asks how to guarantee the quality of service (QoS) of the multicast streams under the Evolved Node B (eNB) while minimizing the UEs’ wake-up time. We prove this problem to be NP-complete and propose an energy-efficient heuristic. This heuristic consists of two phases. The first phase tries to aggregate the required bandwidth of the multicast streams for UEs to reduce their wake-up periods. The second phase further minimizes UEs’ unnecessary wake-up periods by optimizing their DRX configurations. Extensive simulation results show that our scheduling is close to the optimum in most cases.]]>157183918491504