Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362815
Juan Liu, Lin Gao, Tong Wang, Xiao Zeng, Weipeng Lu, Yixuan Zhong
An operator-assisted crowdsourced WiFi community network can provide high-speed wireless data services in an inexpensive way, by encouraging a set of individual users to form a community and share their private home WiFi access points (APs) with others. Such a novel paradigm has shown great promise in achieving the ubiquitous and full coverage networks. In this paper, we perform a systemic analysis for such a community network, where users are heterogeneous in terms of both the network evaluation and the home location popularity. We formulate the interactions between the network operator and users as a non-cooperative game, and focus on the operator's pricing scheme design and the users' behavior analysis. Specifically, we propose a hybrid pricing scheme combining both the fixed price (e.g., the monthly fee) and the usage-based price (proportional to the WiFi connection time) for AP sharing among users. After analyzing users' best response towards the given pricing scheme, we characterize the dynamic changes of the membership distribution over time and indicate the market equilibrium. Simulation results show that under the different pricing schemes and different roaming qualities, the equilibrium social welfare can be increased to 137% to 147%, comparing with the tradition non-crowdsourced system.
{"title":"Crowsourcing: A novel approach to organizing WiFi community networks","authors":"Juan Liu, Lin Gao, Tong Wang, Xiao Zeng, Weipeng Lu, Yixuan Zhong","doi":"10.23919/WIOPT.2018.8362815","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362815","url":null,"abstract":"An operator-assisted crowdsourced WiFi community network can provide high-speed wireless data services in an inexpensive way, by encouraging a set of individual users to form a community and share their private home WiFi access points (APs) with others. Such a novel paradigm has shown great promise in achieving the ubiquitous and full coverage networks. In this paper, we perform a systemic analysis for such a community network, where users are heterogeneous in terms of both the network evaluation and the home location popularity. We formulate the interactions between the network operator and users as a non-cooperative game, and focus on the operator's pricing scheme design and the users' behavior analysis. Specifically, we propose a hybrid pricing scheme combining both the fixed price (e.g., the monthly fee) and the usage-based price (proportional to the WiFi connection time) for AP sharing among users. After analyzing users' best response towards the given pricing scheme, we characterize the dynamic changes of the membership distribution over time and indicate the market equilibrium. Simulation results show that under the different pricing schemes and different roaming qualities, the equilibrium social welfare can be increased to 137% to 147%, comparing with the tradition non-crowdsourced system.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123591979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362885
Bowen Yu, Lingjun Pu, Qinyi Xie, Jingdong Xu
In this paper, we propose MIU, a novel mobile edge computing framework for iot applications in ultra dense networks, via the control of the macro base station. We present a comprehensive framework model, and formulate a joint task offloading, user association and small base station sleeping problem, aiming at minimizing the energy consumptions of network-wide IoT devices and total SBSs while respecting a series of practical constraints. We design an efficient algorithm by invoking dual-decomposition and subgradient method to solve the formulated mixed-integer quadratic programming problem. Extensive simulation results show that our proposed algorithm achieves better performance in energy consumption than several benchmark schemes.
{"title":"Energy efficient scheduling for IoT applications with offloading, user association and BS sleeping in ultra dense networks","authors":"Bowen Yu, Lingjun Pu, Qinyi Xie, Jingdong Xu","doi":"10.23919/WIOPT.2018.8362885","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362885","url":null,"abstract":"In this paper, we propose MIU, a novel mobile edge computing framework for iot applications in ultra dense networks, via the control of the macro base station. We present a comprehensive framework model, and formulate a joint task offloading, user association and small base station sleeping problem, aiming at minimizing the energy consumptions of network-wide IoT devices and total SBSs while respecting a series of practical constraints. We design an efficient algorithm by invoking dual-decomposition and subgradient method to solve the formulated mixed-integer quadratic programming problem. Extensive simulation results show that our proposed algorithm achieves better performance in energy consumption than several benchmark schemes.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114165635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362846
Sudheer Poojary, R. E. Azouzi, E. Altman, Albert Sunny, Imen Triki, Majed Haddad, T. Jiménez, S. Valentin, D. Tsilimantos
Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.
{"title":"Analysis of QoE for adaptive video streaming over wireless networks","authors":"Sudheer Poojary, R. E. Azouzi, E. Altman, Albert Sunny, Imen Triki, Majed Haddad, T. Jiménez, S. Valentin, D. Tsilimantos","doi":"10.23919/WIOPT.2018.8362846","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362846","url":null,"abstract":"Adaptive video streaming improves users' quality of experience (QoE), while using the network efficiently. In the last few years, adaptive video streaming has seen widespread adoption and has attracted significant research effort. We study a dynamic system of random arrivals and departures for different classes of users using the adaptive streaming industry standard DASH (Dynamic Adaptive Streaming over HTTP). Using a Markov chain based analysis, we compute the user QoE metrics: probability of starvation, prefetching delay, average video quality and switching rate. We validate our model by simulations, which show a very close match. Our study of the playout buffer is based on client adaptation scheme, which makes efficient use of the network while improving users' QoE. We prove that for buffer-based variants, the average video bit-rate matches the average channel rate. Hence, we would see quality switches whenever the average channel rate does not match the available video bit rates. We give a sufficient condition for setting the playout buffer threshold to ensure that quality switches only between adjacent quality levels.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129749681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile edge computing (MEC) has emerged as a promising technique to address the challenge arising from the exponentially increasing data traffic. It not only supports mobile users to offload computations but also caches and delivers popular contents to mobile users. In this paper, we aim at designing novel content caching strategies in MEC networks to reduce access latency and improve energy efficiency. First, the distributed content delivery network based on MECs is developed to support users' requests locally. Moreover, based on users' distribution characteristics and MECs' location, a grouping-based and hierarchical collaborative caching strategy is proposed. Simulation results prove that our caching strategy is more efficient than alternative benchmark strategies in terms of average access latency, total energy consumption and content diversity.
{"title":"GHCC: Grouping-based and hierarchical collaborative caching for mobile edge computing","authors":"Dewang Ren, Xiaolin Gui, Wei Lu, Jian An, Huijun Dai, Xin Liang","doi":"10.23919/WIOPT.2018.8362881","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362881","url":null,"abstract":"Mobile edge computing (MEC) has emerged as a promising technique to address the challenge arising from the exponentially increasing data traffic. It not only supports mobile users to offload computations but also caches and delivers popular contents to mobile users. In this paper, we aim at designing novel content caching strategies in MEC networks to reduce access latency and improve energy efficiency. First, the distributed content delivery network based on MECs is developed to support users' requests locally. Moreover, based on users' distribution characteristics and MECs' location, a grouping-based and hierarchical collaborative caching strategy is proposed. Simulation results prove that our caching strategy is more efficient than alternative benchmark strategies in terms of average access latency, total energy consumption and content diversity.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129929428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362884
Kerem Aytaç, Ömer Korçak
Internet of Things (IoT) term has been hyped for years, and takes so many thing's places in everywhere and makes many things to become easier and remotely controllable with smart automations. Human role in such areas are about to be vanished and sensors, actuators, gateways take over the workloads from human-being by generating the values which are desired to be measured, and transferring them within the network. They also take decisions with some preset rules, artificial intelligence or machine learning methods on behalf of humans. Edge computing became a vital as there is a huge amount of requirement of low-latency, extra resources, network restrictions, loose connections, real-time decisions, etc. In quick service restaurants, many waste management and service optimizations are human or paper-based which contains pre-calculated or pre-simulated values. In this paper, we propose an IoT architecture for quick service restaurants and describe various edge computing applications including processing the sensor values, extracting meaningful information, providing data integrity and more importantly learning the data patterns to present predictions, create alerts, or make some intelligent decisions to provide waste minimization and service optimization.
{"title":"IoT edge computing in quick service restaurants","authors":"Kerem Aytaç, Ömer Korçak","doi":"10.23919/WIOPT.2018.8362884","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362884","url":null,"abstract":"Internet of Things (IoT) term has been hyped for years, and takes so many thing's places in everywhere and makes many things to become easier and remotely controllable with smart automations. Human role in such areas are about to be vanished and sensors, actuators, gateways take over the workloads from human-being by generating the values which are desired to be measured, and transferring them within the network. They also take decisions with some preset rules, artificial intelligence or machine learning methods on behalf of humans. Edge computing became a vital as there is a huge amount of requirement of low-latency, extra resources, network restrictions, loose connections, real-time decisions, etc. In quick service restaurants, many waste management and service optimizations are human or paper-based which contains pre-calculated or pre-simulated values. In this paper, we propose an IoT architecture for quick service restaurants and describe various edge computing applications including processing the sensor values, extracting meaningful information, providing data integrity and more importantly learning the data patterns to present predictions, create alerts, or make some intelligent decisions to provide waste minimization and service optimization.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362875
Sumanth Timmadasari, K. P. Naveen, S. Bhashyam
We present results from an extensive simulation study, conducted to understand the properties of coverage and percolation in infrastructure-based wireless networks that comprise sink and relay nodes. Specifically, we compute vacancy (complement of coverage) and percolation probabilities as functions of sink and relay node densities. Further, we identify that the vacancy probability in an alternate model that is motivated from traditional coverage processes, referred to as independent-disc model, constitutes a lower bound for the vacancy in the original infrastructure-based model. For the case of percolation, we identify a threshold boundary (in the space of sink-relay densities pair) where the percolation probability transits rapidly from 0 to 1 (i.e., from no-percolation to full-percolation).
{"title":"Infrastructure-based wireless networks: Coverage and percolation properties","authors":"Sumanth Timmadasari, K. P. Naveen, S. Bhashyam","doi":"10.23919/WIOPT.2018.8362875","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362875","url":null,"abstract":"We present results from an extensive simulation study, conducted to understand the properties of coverage and percolation in infrastructure-based wireless networks that comprise sink and relay nodes. Specifically, we compute vacancy (complement of coverage) and percolation probabilities as functions of sink and relay node densities. Further, we identify that the vacancy probability in an alternate model that is motivated from traditional coverage processes, referred to as independent-disc model, constitutes a lower bound for the vacancy in the original infrastructure-based model. For the case of percolation, we identify a threshold boundary (in the space of sink-relay densities pair) where the percolation probability transits rapidly from 0 to 1 (i.e., from no-percolation to full-percolation).","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127979974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362887
Vaggelis G. Douros, Janne Riihijärvi, P. Mähönen
Cache-enabled networks have received increasing attention in both wired and wireless settings. A big challenge for the operator of such networks is to solve efficiently the content placement problem, i.e., to decide how many caches to deploy in the network and in which nodes. We study the content placement problem for two classes of network optimisation objectives, the first focusing on the minimisation of the sum of the shortest paths and the second capturing the cost vs. benefit trade-off to deploy a cache. We know from the state-of-the-art that, even in small networks with few caches, it is unrealistic to find the optimal solution in a reasonable timescale for similar optimisation problems. In order to cope with this challenge, we present an approach under the prism of network analysis. We introduce a family of lightweight heuristic algorithms that use graph-theoretic metrics that identify the most important nodes of the network. We evaluate the performance of the heuristics using real network datasets, showing that the best heuristics are based on the metrics of betweenness centrality and degree centrality. Finally, we provide a randomised version of the heuristics noticing that the same metrics present again the best performance across the different datasets. Moreover, we find out that, in general, the deterministic version of each heuristic outperforms its randomised version.
{"title":"On the efficiency of lightweight content placement heuristics for cache-enabled networks","authors":"Vaggelis G. Douros, Janne Riihijärvi, P. Mähönen","doi":"10.23919/WIOPT.2018.8362887","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362887","url":null,"abstract":"Cache-enabled networks have received increasing attention in both wired and wireless settings. A big challenge for the operator of such networks is to solve efficiently the content placement problem, i.e., to decide how many caches to deploy in the network and in which nodes. We study the content placement problem for two classes of network optimisation objectives, the first focusing on the minimisation of the sum of the shortest paths and the second capturing the cost vs. benefit trade-off to deploy a cache. We know from the state-of-the-art that, even in small networks with few caches, it is unrealistic to find the optimal solution in a reasonable timescale for similar optimisation problems. In order to cope with this challenge, we present an approach under the prism of network analysis. We introduce a family of lightweight heuristic algorithms that use graph-theoretic metrics that identify the most important nodes of the network. We evaluate the performance of the heuristics using real network datasets, showing that the best heuristics are based on the metrics of betweenness centrality and degree centrality. Finally, we provide a randomised version of the heuristics noticing that the same metrics present again the best performance across the different datasets. Moreover, we find out that, in general, the deterministic version of each heuristic outperforms its randomised version.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121961752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362890
P. Narayanan, L. N. Theagarajan
In this paper, we present an iterative best-response algorithm to compute the Nash equilibrium of a power allocation game in a multiple access channel (MAC), where each user greedily chooses a power allocation policy to maximize their average transmission rate. We consider a MAC where the fading channel gains are assumed to be stationary and ergodic processes, taking values from a finite set. The receiver decodes the message of a user by assuming the messages of the rest of the users as noise. The Shannon capacity of a user is considered to be the achievable rate of that user. A user transmits with a transmit power chosen from a finite set of power values, in a selfish manner, such that their average rate of transmission is maximized. We show that the Nash equilibrium of this game is unique, provided the number of users in the system is sufficiently large, but finite. Under this condition, we also show that the equilibrium policy does not change with more number of users coming into the system. We propose a simple greedy algorithm to compute the Nash equilibrium when the number of users is sufficiently large, but finite. The proposed algorithm does not depend upon the parameters of other users and hence, can be computed without any feedback or side-information from other users. We also present numerical results to illustrate the performance of the proposed algorithm.
{"title":"The invariant Nash equilibrium for stochastic games in multiple access channel","authors":"P. Narayanan, L. N. Theagarajan","doi":"10.23919/WIOPT.2018.8362890","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362890","url":null,"abstract":"In this paper, we present an iterative best-response algorithm to compute the Nash equilibrium of a power allocation game in a multiple access channel (MAC), where each user greedily chooses a power allocation policy to maximize their average transmission rate. We consider a MAC where the fading channel gains are assumed to be stationary and ergodic processes, taking values from a finite set. The receiver decodes the message of a user by assuming the messages of the rest of the users as noise. The Shannon capacity of a user is considered to be the achievable rate of that user. A user transmits with a transmit power chosen from a finite set of power values, in a selfish manner, such that their average rate of transmission is maximized. We show that the Nash equilibrium of this game is unique, provided the number of users in the system is sufficiently large, but finite. Under this condition, we also show that the equilibrium policy does not change with more number of users coming into the system. We propose a simple greedy algorithm to compute the Nash equilibrium when the number of users is sufficiently large, but finite. The proposed algorithm does not depend upon the parameters of other users and hence, can be computed without any feedback or side-information from other users. We also present numerical results to illustrate the performance of the proposed algorithm.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121074741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362814
Xiao Zeng, Lin Gao, Changkun Jiang, Tong Wang, Juan Liu, Baitao Zou
Mobile crowdsensing (MCS) is becoming more and more popular with the increasing demand for various sensory data in many wireless applications. In the traditional server-client MCS system, a central server is often required to handle massive sensory data (e.g., collecting data from users who sense and dispatching data to users who request), hence it may incur severe congestion and high operational cost. In this work, we introduce a peer-to-peer (P2P) based MCS system, where the sensory data is stored in user devices locally and shared among users in an P2P manner. Hence, it can effectively alleviate the burden on the server, by leveraging the communication, computation, and cache resources of massive user devices. We focus on the economic incentive issue arising in the sharing of data among users in such a system, that is, how to incentivize users to share their sensed data with others. To achieve this, we propose a data market, together with a hybrid pricing mechanism, for users to sell their sensed data to others. We first study how would users choose the best way of obtaining desired data (i.e., sensing by themselves or purchasing from others). Then we analyze the user behavior dynamics as well as the data market evolution, by using the evolutionary game theory. We further characterize the users' equilibrium behaviors as well as the market equilibrium, and analyze the stability of the obtained equilibrium.
{"title":"A hybrid pricing mechanism for data sharing in P2P-based mobile crowdsensing","authors":"Xiao Zeng, Lin Gao, Changkun Jiang, Tong Wang, Juan Liu, Baitao Zou","doi":"10.23919/WIOPT.2018.8362814","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362814","url":null,"abstract":"Mobile crowdsensing (MCS) is becoming more and more popular with the increasing demand for various sensory data in many wireless applications. In the traditional server-client MCS system, a central server is often required to handle massive sensory data (e.g., collecting data from users who sense and dispatching data to users who request), hence it may incur severe congestion and high operational cost. In this work, we introduce a peer-to-peer (P2P) based MCS system, where the sensory data is stored in user devices locally and shared among users in an P2P manner. Hence, it can effectively alleviate the burden on the server, by leveraging the communication, computation, and cache resources of massive user devices. We focus on the economic incentive issue arising in the sharing of data among users in such a system, that is, how to incentivize users to share their sensed data with others. To achieve this, we propose a data market, together with a hybrid pricing mechanism, for users to sell their sensed data to others. We first study how would users choose the best way of obtaining desired data (i.e., sensing by themselves or purchasing from others). Then we analyze the user behavior dynamics as well as the data market evolution, by using the evolutionary game theory. We further characterize the users' equilibrium behaviors as well as the market equilibrium, and analyze the stability of the obtained equilibrium.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115654931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-05-07DOI: 10.23919/WIOPT.2018.8362845
Parikshit Hegde, Akshit Kumar, R. Vaze
A single server with variable speed and a finite buffer is considered under a maximum packet drop probability constraint. The cost of processing by the server is a convex function of the speed of the server. If a packet arrives when the buffer is full, it is dropped instantaneously. Given the finite server buffer, the objective is to find the optimal dynamic server speed to minimize the overall cost subject to the maximum packet drop probability constraint. Finding the exact optimal solution is known to be hard, and hence algorithms with provable approximation bounds are considered. We show that if the buffer size is large enough, the proposed algorithm achieves the optimal performance. For arbitrary buffer sizes, constant approximation guarantees are derived for a large class of packet arrival distributions such as Bernoulli, Exponential, Poisson etc.
{"title":"Speed scaling under QoS constraints with finite buffer","authors":"Parikshit Hegde, Akshit Kumar, R. Vaze","doi":"10.23919/WIOPT.2018.8362845","DOIUrl":"https://doi.org/10.23919/WIOPT.2018.8362845","url":null,"abstract":"A single server with variable speed and a finite buffer is considered under a maximum packet drop probability constraint. The cost of processing by the server is a convex function of the speed of the server. If a packet arrives when the buffer is full, it is dropped instantaneously. Given the finite server buffer, the objective is to find the optimal dynamic server speed to minimize the overall cost subject to the maximum packet drop probability constraint. Finding the exact optimal solution is known to be hard, and hence algorithms with provable approximation bounds are considered. We show that if the buffer size is large enough, the proposed algorithm achieves the optimal performance. For arbitrary buffer sizes, constant approximation guarantees are derived for a large class of packet arrival distributions such as Bernoulli, Exponential, Poisson etc.","PeriodicalId":231395,"journal":{"name":"2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114664201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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