Domain-Specific Languages (DSLs) help manage the growing complexity of systems by facilitating their description and execution or simulation via tailored languages. A large part of the development costs of a DSL comes...
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(纸本)9798350324983
Domain-Specific Languages (DSLs) help manage the growing complexity of systems by facilitating their description and execution or simulation via tailored languages. A large part of the development costs of a DSL comes from building the associated tools it requires, such as an editor or a debugger. To reduce these costs, the Language Server protocol (LSP) and Debug Adapter protocol (DAP) enable the creation of generic tooling interfaces which rely on standardized services exposed by languages. However, as these protocols have been designed for General Purpose Languages (GPLs), their applicability to DSLs has no yet been extensively studied. In this paper, we analyze both LSP and DAP, with an emphasis regarding their relevance for the development of tooling for DSLs. We provide both a high-level insight into these protocols, such as a dependency graph of their services, and a more fine-grained qualitative analysis of each service. We show that while some services defined by these two protocols can be provided by any DSL, others make strong assumptions on the concepts that should be part of the considered DSL. Conversely, domain-specific concepts available in some DSLs are not exploitable through these protocols, thus reducing the capabilities of generic tools.
A fundamental question in the study of location-based mobile sensing is how much energy can be saved while still guaranteeing the reliable localization accuracy. In this paper, we analyze key features of human proximi...
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A fundamental question in the study of location-based mobile sensing is how much energy can be saved while still guaranteeing the reliable localization accuracy. In this paper, we analyze key features of human proximity and find a motivation which implies that the energy-efficient and accurate localization is possible by sharing the locations of nearby mobile devices. From the location-based collaborative sensing idea, we formulate an optimization problem which aims to minimize the total number of location measurements for a given fairness criterion. Then, we propose a practical and distributed location sharing (DLS) protocol and an optimal parameter control algorithm (OWD) which makes the DLS protocol attain an asymptotic optimal performance. Via extensive simulations under various environments including real mobility traces, we verify that the proposed DLS+OWD policy significantly reduces the average power consumption of mobile devices with a higher fairness compared to the existing algorithms.
Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this informatio...
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Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.
Wireless sensor networks (WSNs), often adhering to a single gateway architecture, constitute the communication backbone for many modern cyber-physical systems (CPSs). Consequently, fault-tolerance in CPS becomes a cha...
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Wireless sensor networks (WSNs), often adhering to a single gateway architecture, constitute the communication backbone for many modern cyber-physical systems (CPSs). Consequently, fault-tolerance in CPS becomes a challenging task, especially when accounting for failures (potentially malicious) that incapacitate the gateway or disrupt the nodes-gateway communication, not to mention the energy, timeliness, and security constraints demanded by CPS domains. This paper aims at ameliorating the fault-tolerance of WSN-based CPS to increase system and data availability. To this end, we propose a replicated gateway architecture augmented with energy-efficient real-time Byzantine-resilient data communication protocols. At the sensors level, we introduce fault-tolerant trustful space-time protocol, a geographic routing protocol capable of delivering messages in an energy-efficient and timely manner to multiple gateways, even in the presence of voids caused by faulty and malicious sensor nodes. At the gateway level, we propose a multigateway synchronization protocol, which we call ByzCast, that delivers timely correct data to CPS applications, despite the failure or maliciousness of a number of gateways. We show, through extensive simulations, that our protocols provide better system robustness yielding an increased system and data availability while meeting CPS energy, timeliness, and security demands.
One of the most critical emerging problems for 5G and Internet of Things is the handling of machine-to-machine communication. Wireless sensor networks are deployed every day, resulting in a more distributed infrastruc...
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One of the most critical emerging problems for 5G and Internet of Things is the handling of machine-to-machine communication. Wireless sensor networks are deployed every day, resulting in a more distributed infrastructure, where the communication and processing are handled by energy, bandwidth, and processing constrained devices. Aggregation of multiple packets flowing over the same path increases spectral efficiency, energy efficiency, and resource utilization. We address the problem of determining the optimal waiting time to maximize the utility within the network. We provide a general framework, where the utility function is user-defined for each individual application stream and packet. This allows the user to optimize for energy, delay, or expiration rate in the resolution of individual streams. Our algorithm calculates the optimal time for any given condition on-the-fly and can adapt to changing conditions with low computational complexity. We provide an optimal multi-hop distributed and scalable under congestion versions of our algorithm. Our simulations in ns3 show that we outperform state-of-the-art policies by 1.55x in terms of information freshness. Our solution reduces average power consumption by more than 60 percent. Our congestion-aware solution shows constant performance with increasing congestion levels, whereas state-of-the-art solutions degrade by up to 70 percent under the same conditions.
The advances in mobile device and wireless communication techniques have enabled anywhere, anytime information access. Information being accessed, whether structured or unstructured, can be classified into three categ...
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The advances in mobile device and wireless communication techniques have enabled anywhere, anytime information access. Information being accessed, whether structured or unstructured, can be classified into three categories: private data, shared data, and public data. Private and shared data are usually accessed through on-demand-based services, where user request is pushed to the server(s) and response to the request is generated and passed to the user (i.e., two way communication). Public data, on the other hand, can be most effectively disseminated using broadcasting technique (i.e., one way communication). In broadcasting, server(s) generates the broadcast contents and disseminate it through the air channel. The mobile user in search of public data, tunes to the air channel and pulls the desired information. The characteristics of mobile device and limitations of wireless communication technology pose challenges on broadcasting strategy as well as data retrieval algorithms. To reduce the access time and power consumption, major research issues include indexing techniques and data organization on air channel, broadcasting over single and parallel channel(s), data distribution and replication strategy, conflict resolution, and data retrieval methods This article is intended to articulate these challenges, propose several solutions, and through comprehensive simulation demonstrate the validity of the proposed solutions. (C) 2017 Elsevier Inc. All rights reserved.
The emerging use of multi-homed wireless devices along with simultaneous multi-path data transfer offers tremendous potentials to improve the capacity of multi-hop wireless networks. The use of simultaneous data trans...
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The emerging use of multi-homed wireless devices along with simultaneous multi-path data transfer offers tremendous potentials to improve the capacity of multi-hop wireless networks. The use of simultaneous data transfer over separate disjoint paths in multi-hop wireless networks to increase network capacity is a less explored subject, mainly because of the challenges it triggers for the reliable transport layer protocols. Reliable transport layer protocols generally use packet sequence number as a mean to ensure delivery. As such, the out-of-order packet arrival in reliable transport layer protocols triggers receiver buffer blocking that causes throughput degradation and prevents the reliable multi-path transport layer protocol to realize its vast potential. This paper focuses on integrating network coding with a reliable multi-path transport layer protocol to resolve the receiver buffer blocking problem. We propose an adaptive network coding mechanism to desensitize the receiver against packet reordering and consequently eliminate the receiver buffer blocking problem. Our state-of-the-art network coding scheme uses a combination of Q-learning and logistic regression for rare data events to control the number of redundant packets based on the network dynamics. We confirmed the veracity of our proposed scheme by a queuing theory based mathematical model. Moreover, the effectiveness of the proposed scheme is demonstrated through simulations and testbed experiments.
Constraints on the energy, bandwidth, and connectivity of mobile devices and wireless communication medium complicate the timely and reliable access to public data. Energy is often the most stringent constraint, neces...
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Constraints on the energy, bandwidth, and connectivity of mobile devices and wireless communication medium complicate the timely and reliable access to public data. Energy is often the most stringent constraint, necessitating techniques that facilitate operation in energy-saving modes. Broadcasting, typically over parallel channels, has proven to be an effective method for dissemination of public data to mobile devices. However, the employment of parallel channels introduces challenges associated with channel switching and conflicts due to concurrent accesses to multiple data items that ultimately increase energy consumption and response time. The detrimental effects on energy consumption and response time can be alleviated by scheduling the retrieval of data items in an order that reduces the number of passes over the air channels and channel switching between the parallel channels. In this paper, several scheduling algorithms are proposed and analyzed that achieve the aforementioned objectives. To further improve energy consumption and response time, the scope of our scheduling algorithms has been enhanced by replication of popular data items. The proposed scheduling algorithms, both with and without replication, have been simulated, and simulation results are presented and analyzed. These results show that the proposed scheduling algorithms, compared to some heuristic based methods, have greater impact in reducing energy consumption and response time. This reduction is shown to be more-pronounced with replication of data items. (C) 2016 Elsevier Inc. All rights reserved.
Recent work has examined techniques to estimate the "best" modulation rate for data networks such as 802.11a/g. While accurate rate estimation yields better rate-selection decisions and increased throughput,...
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Recent work has examined techniques to estimate the "best" modulation rate for data networks such as 802.11a/g. While accurate rate estimation yields better rate-selection decisions and increased throughput, those methods must still choose between a handful of modulation rates. Each modulation rate is effective in a range of actual signal-to-noise ratios (SNRs) but the limited number of practical rates means that transmitters are often forced to "step down" to a lower data rate despite having a higher SNR than the minimum required for that lower rate. In this paper we describe, evaluate and implement a practical multiuser communication scheme that exploits these discrete "steps" in modulation rates to transmit two packets in the time normally needed to transmit a single packet, increasing aggregate throughput precisely when it is most needed-when the network is busy and suffers from rate unfairness. Because the method transmits a group of packets simultaneously, we call this scheme Group Rate Transmission with Intertwined Symbols, or GRaTIS. In addition to up to 120% improvement in network throughput achieved by GRaTIS, the technique is backward compatible with 802.11 and doesn't require complex DSP algorithms as required by competing methods.
Recent advances in connected vehicles and autonomous driving are going to change the face of ground trans- portation as we know it. This paper describes the design and evaluation of several emerging applications for s...
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Recent advances in connected vehicles and autonomous driving are going to change the face of ground trans- portation as we know it. This paper describes the design and evaluation of several emerging applications for such a cyber transportation system (CTS). These applications have been designed using holistic approaches, which consider the unique roles played by the human drivers, the transportation system, and the communication network. They can improve driver safety and provide on-road infotainment. They can also improve transportation operations and efficiency, thereby benefiting travelers and attracting investment from both government agencies and private businesses to deploy infrastructures and bootstrap the evolutionary process of CTS.
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