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

Integrating wireless technologies into intra-vehicular communication

With the emergence of connected and autonomous vehicles, sensors are increasingly deployed within car. Traffic generated by these sensors congest traditional intra-vehicular networks, such as CAN buses. Furthermore, the large amount of wires needed to connect sensors makes it hard to design cars in a modular way. These limitations have created impetus to use wireless technologies to support intra-vehicular communication. In this dissertation, we tackle the challenge of designing and evaluating data collection protocols for intra-car networks that can operate reliably and efficiently under dynamic channel conditions. First, we evaluate the feasibility of deploying an intra-car wireless network based on the Backpressure Collection Protocol (BCP), which is theoretically proven to be throughput-optimal. We uncover a surprising behavior in which, under certain dynamic channel conditions, the average packet delay of BCP decreases with the traffic load. We propose and analyze a queueing-theoretic model to shed light into the observed phenomenon. As a solution, we propose a new protocol, called replication-based LIFO-backpressure (RBL). Analytical and simulation results indicate that RBL dramatically reduces the delay of BCP at low load, while maintaining its high throughput performance. Next, we propose and implement a hybrid wired/wireless architecture, in which each node is connected to either a wired interface or a wireless interface or both. We propose a new protocol, called Hybrid-Backpressure Collection Protocol (Hybrid-BCP), for the intra-car hybrid networks. Our testbed implementation, based on CAN and ZigBee transceivers, demonstrates the load balancing and routing functionalities of Hybrid-BCP and its resilience to DoS attacks. We further provide simulation results, obtained based on real intra-car RSSI traces, showing that Hybrid-BCP can achieve the same performance as a tree-based protocol while reducing the radio transmission power by a factor of 10. Finally, we present TeaCP, a prototype Toolkit for the evaluation and analysis of Collection Protocols in both simulation and experimental environments. TeaCP evaluates a wide range of standard performance metrics, such as reliability, throughput, and latency. TeaCP further allows visualization of routes and network topology evolution. Through simulation of an intra-car WSN and real lab experiments, we demonstrate the functionality of TeaCP for comparing different collection protocols

A PERSISTENT SCHEME FOR MAPPING ROUTE QUERYING ON SHAPELESS PEER NETWORKS

Among the principal functions of the systems is earnestly resolving queries or data files/sources. This is absolutely the complication addressed in a period this study. In create systems, peers/files/sources are tabulated to start overlays with specialized geopolitics and qualities. Locating a detail or capital not outside a disorganized peer-to-peer chain is unquestionably an inordinately embarrassing situation. The program is proven to come sustain the quiz load prone to a category and services message inhibition, i.e., a guarantee that queries' routes meet pre-specified class-stationed bounds on their own united deduced action of quiz finding. The inefficiencies of quite unregulated systems likely partly addressed by amalgam P2P systems. Additional aspects associated with shortening convolution, guessing parameters, and variation to class-planted quiz proposal odds and trade loads are planned. The job of proposes a planning site peer’s hideout the finished product of past queries as knowledgeable by reverse-path forwarding. This structure involves substantial overhead, isn't load hypersensitive, and hasn't yet addicted guarantees on opera. An exact likeness from the capability field for such systems is offered and numerically fit it linked with indiscriminate walk occupying searches

LifeTime-aware Backpressure - a new delay-enhanced Backpressure-based routing protocol

Dynamic Backpressure is a highly desirable family of routing protocols known for their attractive mathematical proprieties. However, these protocols suffer from a high end-to-end delay making them inefficient for real-time traffic with strict endto-end delay requirements. In this paper, we address this issue by proposing a new adjustable and fully distributed Backpressurebased scheme with low queue management complexity, named LifeTime-Aware BackPressure (LTA-BP). The novelty in the proposed scheme consists in introducing the urgency level as a new metric for service differentiation among the competing traffic flows in the network. Our scheme not just significantly improves the quality of service provided for real-time traffic with stringent end-to-end delay constraints, but interestingly protects also the flows with softer delay requirements from being totally starved. The proposed scheme has been evaluated and compared against other state of the art routing protocol, using computer simulation, and the obtained results show its superiority in terms of the achieved end-to-end delay and throughput

Adaptive Matching for Expert Systems with Uncertain Task Types

A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts as the expert resources are often scarce. The efficient utilization of experts in these platforms is made challenging by the fact that the information available about the parties involved is usually limited. To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches. In our model the tasks arrive online while the experts are fixed and constrained by a finite service capacity. For this model, we characterize the maximum task resolution throughput a platform can achieve. We show that the natural greedy approaches where each expert is assigned a task most suitable to her skill is suboptimal, as it does not internalize the above externality. We develop a throughput optimal backpressure algorithm which does so by accounting for the `congestion' among different task types. Finally, we validate our model and confirm our theoretical findings with data-driven simulations via logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics.Comment: A part of it presented at Allerton Conference 2017, 18 page

A DEPENDABLE PROCEDURE IN PURSUANCE OF ROUTE QUERIES IN OPEN P2P NETWORKS

One of the key functions of these programs is to effectively solve questions or search for files / resources. This is actually a problem addressed to this paper. For organized programs, colleagues / files / sources are organized to create a set of specific problems and attributes. Finding a document or service within an unwanted network is very difficult. This approach has been shown to reinforce the query burden in the services and services sector, which means that the query paths are consistent with the boundaries of the same category that are related to the layer being resolved. Unused applications may follow P2P hybrid programs. Additional features are taken with respect to limiting restrictions, measuring boundaries, and tuning classroom-based queries and car loads. Employee promotion strategy where peers hide recent query results as described in the redirection process. This method includes high quality, no risk, and no guarantee yet. Exposure is provided from the rope area of ​​these programs and corresponds to the number associated with random searches

Backpressure meets taxes: Faithful data collection in stochastic mobile phone sensing systems

The use of sensor-enabled smart phones is considered to be a promising solution to large-scale urban data collection. In current approaches to mobile phone sensing systems (MPSS), phones directly transmit their sensor readings through cellular radios to the server. However, this simple solution suffers from not only significant costs in terms of energy and mobile data usage, but also produces heavy traffic loads on bandwidth-limited cellular networks. To address this issue, this paper investigates cost-effective data collection solutions for MPSS using hybrid cellular and opportunistic short-range communications. We first develop an adaptive and distribute algorithm OptMPSS to maximize phone user financial rewards accounting for their costs across the MPSS. To incentivize phone users to participate, while not subverting the behavior of OptMPSS, we then propose BMT, the first algorithm that merges stochastic Lyapunov optimization with mechanism design theory. We show that our proven incentive compatible approaches achieve an asymptotically optimal gross profit for all phone users. Experiments with Android phones and trace-driven simulations verify our theoretical analysis and demonstrate that our approach manages to improve the system performance significantly (around 100%) while confirming that our system achieves incentive compatibility, individual rationality, and server profitability