Intelligent traffic signal control is an effective way to solve the traffic congestion problem in the real world. One trend is to use Deep Reinforcement Learning (DRL) to control traffic signals based on the snapshots of traffic states. While most of the research used single numeric reward to frame multiple objectives, such as minimizing waiting time and waiting queue length, they overlooked that one reward for multiple objectives misleads agents taking wrong actions in certain states, which causes following traffic fluctuation. In this paper, we propose a DRL-based framework that uses multiple rewards for multiple objectives. Our framework aims to solve the difficulty of assessing behaviours by single numeric reward and control traffic flows more steadily. We evaluated our approach on both synthetic traffic scenarios and a real-world traffic dataset in Toronto. The results show that our approach outperformed single reward-based approaches.
{"title":"Traffic Signal Control Using Deep Reinforcement Learning with Multiple Resources of Rewards","authors":"Dunhao Zhong, A. Boukerche","doi":"10.1145/3345860.3361522","DOIUrl":"https://doi.org/10.1145/3345860.3361522","url":null,"abstract":"Intelligent traffic signal control is an effective way to solve the traffic congestion problem in the real world. One trend is to use Deep Reinforcement Learning (DRL) to control traffic signals based on the snapshots of traffic states. While most of the research used single numeric reward to frame multiple objectives, such as minimizing waiting time and waiting queue length, they overlooked that one reward for multiple objectives misleads agents taking wrong actions in certain states, which causes following traffic fluctuation. In this paper, we propose a DRL-based framework that uses multiple rewards for multiple objectives. Our framework aims to solve the difficulty of assessing behaviours by single numeric reward and control traffic flows more steadily. We evaluated our approach on both synthetic traffic scenarios and a real-world traffic dataset in Toronto. The results show that our approach outperformed single reward-based approaches.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90971231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. León, Thomas Begin, A. Busson, Luis J. de la Cruz Llopis
The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers' homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new distributed congestion control mechanism is proposed, implemented and evaluated for NANs. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The main goal is to provide with the needed Quality of Service (QoS) to all traffic flows, especially in high traffic load situations. The proposal is evaluated in the context of a wireless ad hoc network made up by a set of smart meter devices, using the Ad hoc On-Demand Distance Vector (AODV) routing protocol and the IEEE 802.11ac physical layer standard. The application of the proposed congestion control mechanism, together with the necessary modifications made to the AODV protocol, lead to performance improvements in terms of packet delivery ratio, network throughput and transit time, fairness between different traffic sources and QoS provision.
{"title":"Towards a Distributed Congestion Control mechanism for Smart Grid Neighborhood Area Networks","authors":"J. León, Thomas Begin, A. Busson, Luis J. de la Cruz Llopis","doi":"10.1145/3345860.3361520","DOIUrl":"https://doi.org/10.1145/3345860.3361520","url":null,"abstract":"The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers' homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new distributed congestion control mechanism is proposed, implemented and evaluated for NANs. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The main goal is to provide with the needed Quality of Service (QoS) to all traffic flows, especially in high traffic load situations. The proposal is evaluated in the context of a wireless ad hoc network made up by a set of smart meter devices, using the Ad hoc On-Demand Distance Vector (AODV) routing protocol and the IEEE 802.11ac physical layer standard. The application of the proposed congestion control mechanism, together with the necessary modifications made to the AODV protocol, lead to performance improvements in terms of packet delivery ratio, network throughput and transit time, fairness between different traffic sources and QoS provision.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80461743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-08DOI: 10.5772/INTECHOPEN.84288
Jafar Rasouli, S. Motamedi, Mohamad Baseri, Mahshad Parsa
Creating cyber-physical systems (CPSs) based on wireless sensor and actuator networks (WSANs) has great potential to improve the performance of Smart Grid. In addition, IEEE802.15.4 has widely been regarded as an appropriate standard for WSANs, due to some striking and unique features. WSANs require provisioning strict quality of service (QoS) due to noisy harsh environments in Smart Grid applications. Although analytical models have been studied in the literature, they have not provided a full-fledged model for Smart Grid. In this paper, we have added a MAC-level buffer, and a novel Markov chain model has been also proposed. By comparison with previous studies, retransmission confines, acknowledgment, packet length variation, saturated traffic, and degenerate distribution of packet generation are accounted for. The algorithm has been experimentally implemented and appraised on a platform with self-designed WSAN. The analytical model predicts well our exhaustive experiments. Further, Monte Carlo simulations validate mathematical results.
{"title":"A Reliable Communication Model based on IEEE802.15.4 for WSANs in Smart Grids","authors":"Jafar Rasouli, S. Motamedi, Mohamad Baseri, Mahshad Parsa","doi":"10.5772/INTECHOPEN.84288","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84288","url":null,"abstract":"Creating cyber-physical systems (CPSs) based on wireless sensor and actuator networks (WSANs) has great potential to improve the performance of Smart Grid. In addition, IEEE802.15.4 has widely been regarded as an appropriate standard for WSANs, due to some striking and unique features. WSANs require provisioning strict quality of service (QoS) due to noisy harsh environments in Smart Grid applications. Although analytical models have been studied in the literature, they have not provided a full-fledged model for Smart Grid. In this paper, we have added a MAC-level buffer, and a novel Markov chain model has been also proposed. By comparison with previous studies, retransmission confines, acknowledgment, packet length variation, saturated traffic, and degenerate distribution of packet generation are accounted for. The algorithm has been experimentally implemented and appraised on a platform with self-designed WSAN. The analytical model predicts well our exhaustive experiments. Further, Monte Carlo simulations validate mathematical results.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2019-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79997935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1007/978-981-15-1785-3
Phoebe Chen, A. Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, K. Sivalingam, D. Ślęzak, T. Washio, Xiaokang Yang, Junsong Yuan, Simone Diniz Junqueira Barbosa, Songtao Guo, Kai Liu, Chao Chen, Hongyu Huang
{"title":"Wireless Sensor Networks: 13th China Conference, CWSN 2019, Chongqing, China, October 12–14, 2019, Revised Selected Papers","authors":"Phoebe Chen, A. Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, K. Sivalingam, D. Ślęzak, T. Washio, Xiaokang Yang, Junsong Yuan, Simone Diniz Junqueira Barbosa, Songtao Guo, Kai Liu, Chao Chen, Hongyu Huang","doi":"10.1007/978-981-15-1785-3","DOIUrl":"https://doi.org/10.1007/978-981-15-1785-3","url":null,"abstract":"","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87021665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"FACTS2","authors":"T. Gräupl, Nils Mäurer, C. Schmitt","doi":"10.1145/3345860.3365111","DOIUrl":"https://doi.org/10.1145/3345860.3365111","url":null,"abstract":"","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77229577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The sparse basis of signals plays a key role in signals processing of wireless sensor networks (WSNs). However, the existing sparse bases, such as principal component analysis (PCA) and discrete cosine transform (DCT), do not support a good recovery effect in WSNs. In this paper, the general K-SVD (K-Means Singular Value Decomposition) is optimized and a new adaptive overcomplete dictionary (K-SVD-DCT) is constructed by extracting features of distributed WSN signals. First of all, we normalize the data and select the DCT matrix as the initial training dictionary D of the K-SVD algorithm, and then use the orthogonal matching pursuit (OMP) method to carry out sparse decomposition on signals, obtaining the sparse representation matrix. Then the dictionary atom is upgraded by iterating D. Eventually, K-SVD-DCT for sensor network signals' sparse representation is obtained after multiple iterations. We evaluate the performances of overcomplete dictionaries constructed by three initial training dictionaries. The experimental results show that the recovery errors of using the K-SVD-DCT are smaller than that of the PCA basis and are similar to that of the DCT basis. However, the successful recovery rate (8.0%) of the DCT basis is much lower than that of the K-SVD-DCT (82%).
信号的稀疏基在无线传感器网络信号处理中起着至关重要的作用。然而，现有的稀疏基，如主成分分析(PCA)和离散余弦变换(DCT)，在wsn中并不能支持良好的恢复效果。本文对广义K-SVD (K-Means Singular Value Decomposition)进行了优化，并通过提取分布式WSN信号的特征，构造了一种新的自适应过完备字典(K-SVD- dct)。首先对数据进行归一化处理，选取DCT矩阵作为K-SVD算法的初始训练字典D，然后利用正交匹配追踪(OMP)方法对信号进行稀疏分解，得到稀疏表示矩阵。然后通过迭代d对字典原子进行升级，最终经过多次迭代得到传感器网络信号稀疏表示的K-SVD-DCT。我们评估了由三个初始训练字典构造的过完备字典的性能。实验结果表明，使用K-SVD-DCT的恢复误差小于PCA基，与DCT基相似。然而，DCT基础的成功率(8.0%)远低于K-SVD-DCT的成功率(82%)。
{"title":"Sparse Representation of Sensor Network Signals Based on the K-SVD Algorithm","authors":"Z. Zou, Xu He, Yinxia Wang, Jiagao Wu","doi":"10.1145/3243046.3243061","DOIUrl":"https://doi.org/10.1145/3243046.3243061","url":null,"abstract":"The sparse basis of signals plays a key role in signals processing of wireless sensor networks (WSNs). However, the existing sparse bases, such as principal component analysis (PCA) and discrete cosine transform (DCT), do not support a good recovery effect in WSNs. In this paper, the general K-SVD (K-Means Singular Value Decomposition) is optimized and a new adaptive overcomplete dictionary (K-SVD-DCT) is constructed by extracting features of distributed WSN signals. First of all, we normalize the data and select the DCT matrix as the initial training dictionary D of the K-SVD algorithm, and then use the orthogonal matching pursuit (OMP) method to carry out sparse decomposition on signals, obtaining the sparse representation matrix. Then the dictionary atom is upgraded by iterating D. Eventually, K-SVD-DCT for sensor network signals' sparse representation is obtained after multiple iterations. We evaluate the performances of overcomplete dictionaries constructed by three initial training dictionaries. The experimental results show that the recovery errors of using the K-SVD-DCT are smaller than that of the PCA basis and are similar to that of the DCT basis. However, the successful recovery rate (8.0%) of the DCT basis is much lower than that of the K-SVD-DCT (82%).","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74550571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session 4: Wireless Sensor Networks","authors":"Rodolfo W. L. Coutinho","doi":"10.1145/3289323","DOIUrl":"https://doi.org/10.1145/3289323","url":null,"abstract":"","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80500785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Luis Zabala, A. Ferro, Rubén Solozabal, Bego Blanco
This paper describes a modeling based on Generalized Stochastic Petri Nets (GSPN) to analyze the performance of a network probing node in terms of throughput. The probing node is part of a distributed monitoring system. In this environment, the use of multiprocessor and multicore systems, as well as the parallelization of applications, is aimed at improving the node performance. Petri nets allow not only to represent the parallelization feature, but also to include the main events identified in the system: the packet arrival and a two-stage processing. The two-stage processing consists of a first stage in which packet capturing functionalities are performed, and a second stage in which a deeper packet treatment is performed. In addition, the Petri net model can reproduce a shared buffer control mechanism to ensure the integrity of the data. After detailing all the model components, the verification and validation of the model are done by using a simulation tool. With this model, it is expected to estimate the efficiency of the probing node early in the design and development stages.
{"title":"Performance Analysis of a Network Sensor's Packet Processing System using Generalized Stochastic Petri Nets","authors":"Luis Zabala, A. Ferro, Rubén Solozabal, Bego Blanco","doi":"10.1145/3243046.3243051","DOIUrl":"https://doi.org/10.1145/3243046.3243051","url":null,"abstract":"This paper describes a modeling based on Generalized Stochastic Petri Nets (GSPN) to analyze the performance of a network probing node in terms of throughput. The probing node is part of a distributed monitoring system. In this environment, the use of multiprocessor and multicore systems, as well as the parallelization of applications, is aimed at improving the node performance. Petri nets allow not only to represent the parallelization feature, but also to include the main events identified in the system: the packet arrival and a two-stage processing. The two-stage processing consists of a first stage in which packet capturing functionalities are performed, and a second stage in which a deeper packet treatment is performed. In addition, the Petri net model can reproduce a shared buffer control mechanism to ensure the integrity of the data. After detailing all the model components, the verification and validation of the model are done by using a simulation tool. With this model, it is expected to estimate the efficiency of the probing node early in the design and development stages.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85315990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul Detterer, Cumhur Erdin, Majid Nabi, T. Basten, Hailong Jiao
Energy efficiency is of paramount importance in designing lowpower wireless sensor nodes. Approximate computing is a new circuit-level technique for reducing power consumption. However, the gain in power by applying this technique is achieved at the cost of computational errors. The impact of such inaccuracies in the circuit level of a radio transceiver chip on the performance of Wireless Sensor Networks (WSNs) has not yet been explored. The applicability of such low-power chip design techniques depends on the overall energy gain and their impact on the network performance. In this paper, we analyze various inaccuracy fields in a radio chip, and quantify their impact on the network performance, in terms of packet latency, goodput, and energy per bit. The analysis is supported by extensive network simulations. The outcome can be used to investigate in which WSN application scenarios such power reduction techniques at circuit level can be applied, given the network performance and energy consumption requirements.
{"title":"Understanding the Impact of Circuit-Level Inaccuracy on Sensor Network Performance","authors":"Paul Detterer, Cumhur Erdin, Majid Nabi, T. Basten, Hailong Jiao","doi":"10.1145/3243046.3243062","DOIUrl":"https://doi.org/10.1145/3243046.3243062","url":null,"abstract":"Energy efficiency is of paramount importance in designing lowpower wireless sensor nodes. Approximate computing is a new circuit-level technique for reducing power consumption. However, the gain in power by applying this technique is achieved at the cost of computational errors. The impact of such inaccuracies in the circuit level of a radio transceiver chip on the performance of Wireless Sensor Networks (WSNs) has not yet been explored. The applicability of such low-power chip design techniques depends on the overall energy gain and their impact on the network performance. In this paper, we analyze various inaccuracy fields in a radio chip, and quantify their impact on the network performance, in terms of packet latency, goodput, and energy per bit. The analysis is supported by extensive network simulations. The outcome can be used to investigate in which WSN application scenarios such power reduction techniques at circuit level can be applied, given the network performance and energy consumption requirements.","PeriodicalId":55557,"journal":{"name":"Ad Hoc & Sensor Wireless Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2018-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86505286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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