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  • 1.
    Du, Rong
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Optimal Networking in Wirelessly Powered Sensor Networks2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Wireless sensor networks (WSNs) are nowadays widely used for the long-term monitoring of small or large regions, such as lakes, forests, cities, and industrial areas. The performance of a WSN typically consists of two aspects: i) the monitoring performance, e.g., the accuracy and the timeliness of the measurements or estimations produced by the sensor nodes of the WSN; and ii) the lifetime, i.e., how long the WSN can sustain such a performance. Naturally, we would like to have the monitoring performance as good as possible, and the lifetime as long as possible. However, in traditional WSNs, the sensor nodes generally have limited resources, especially in terms of battery capacity. If the nodes make measurements and report them frequently for a good monitoring performance, they drain their batteries and  this leads to a severely shortened network lifetime. Conversely, the sensors can have a longer lifetime by sacrificing the monitoring performance. It shows the inherent trade-off between the monitoring performance and the lifetime in WSNs.

    We can overcome the limitations of the trade-off described above by wireless energy transfer (WET), where we can provide the sensor nodes with additional energy remotely. The WSNs with WET are called wirelessly powered sensor networks (WPSNs). In a WPSN, dedicated energy sources, e.g., static base stations or mobile chargers, transmit energy via radio frequency (RF) waves to the sensor nodes. The nodes can store the energy in their rechargeable batteries and use it later when it is needed. In so doing, they can use more energy to perform the sensing tasks. Thus, WET is a solution to improve the monitoring performance and lifetime at the same time.  As long as the nodes receive more energy than they consume, it is possible that the WSN be immortal, which is impossible in traditional WSNs. 

    Although WPSNs can potentially break the trade-off between monitoring performance and lifetime, they also bring many fundamental design and performance analysis challenges. Due to the safety issues, the power that the dedicated energy sources can use is limited. The propagation of the RF waves suffers high path losses. Therefore, the energy received by the sensor nodes is much less than the energy transmitted from the sources. As a result, to have a good WSN performance, we should optimize the energy transmission on the energy source side and the energy consumption on the nodes side. Compared to the traditional WSN scenarios where we can only optimize the sensing and data communication strategies, in WPSNs, we have an additional degree of freedom, i.e., the optimization of the energy transmission strategies. This aspect brings new technical challenges and problems that have not been studied in the traditional WSNs. Several novel research questions arise, such as when and how to transmit the energy, and which energy source should transmit. Such questions are not trivial especially when we jointly consider the energy consumption part.

    This thesis contributes to answer the questions above. It consists of three contributions as follows.

    In the first contribution, we consider a WPSN with single energy base stations (eBS) and multiple sensor nodes to monitor several separated areas of interest. The eBS has multiple antennas, and it uses energy beamforming to transmit energy to the nodes. Notice that, if we deploy multiple sensor nodes at the same area, these nodes may receive the energy from the eBS at the same time and they can reduce the energy consumption by applying sleep/awake mechanism. Therefore, we jointly study the deployment of the nodes, the energy transmission of the eBS, and the node activation. The problem is an integer optimization, and we decouple the problem into a node deployment problem and a scheduling problem. We provide a greedy-based algorithm to solve the problem, and show its performance in terms of optimality.

    The second contribution of the thesis starts by noticing that wireless channel state information (CSI) is important for energy beamforming. The more energy that an eBS spends in channel acquisition, the more accurate CSI it will have, thus improving the energy beamforming performance. However, if the eBS spends too much energy on channel acquisition, it will have less energy for WET, which might reduce the energy that is received by the sensor nodes. We thus investigate how much energy the eBS should spend in channel acquisition, i.e., we study the power allocation problem in channel acquisition and energy beamforming for WPSNs. We consider the general optimal channel acquisition and show that the problem is non-convex. Based on the idea of bisection search, we provide an algorithm to find the optimal solution for the single eBS cases, and a closed-form solution for the case where the eBS uses orthogonal pilot transmission, least-square channel estimation, and maximum ratio transmission for WET. The simulations show that the algorithm converges fast, and the performance is close to the theoretical upper bound.

    In the third contribution, we consider a joint energy beamforming and data routing problem for WPSNs. More specifically, we investigate the WPSNs consisting of multiple eBSs, multiple sensor nodes, and a sink node. Based on the received energy, the sensor nodes need to decide how to route their data. The problem aims at maximizing the minimum sensing rate of the sensor nodes while guaranteeing that the received energy of each node is no less than that is consumed. Such a problem is non-convex, and we provide a centralized solution algorithm based on a semi-definite programming transformation. We extend this approach with a distributed algorithm using alternating direction method of multipliers (ADMM). We prove that the centralized algorithm achieves the optimal energy beamforming and routing, and we show by simulation that the distributed one converges to the optimal solution. Additionally, for the cases where the energy beamforming options are pre-determined, we study the problem of finding the energy that should be spent on each vector. We observe that, if the pre-determined beamforming options are chosen wisely, their performance is close to the optimal.

    The results of the thesis show that WET can prolong the lifetime of WSNs, and even make them work sufficiently long for general monitoring applications. More importantly, we should optimize the WPSN by considering both the energy provision and the energy consumption part. The studies of the thesis have the potential to be used in many Internet of Things (IoT) systems in smart cities, such as water distribution lines and building monitoring.

  • 2. Du, Rong
    et al.
    Chen, Cailian
    Yang, Bo
    Lu, Ning
    Guan, Xinping
    Xuemin, Shen
    Effective Urban Traffic Monitoring by Vehicular Sensor Networks2015In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 64, no 1, p. 273-286Article in journal (Refereed)
    Abstract [en]

    Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks. Probe vehicles (PVs), such as taxis and buses, and floating cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to a traffic-monitoring center (TMC) for traffic estimation. In the TMC, sensing reports are aggregated to form a traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads all the time, the TMC needs to estimate the unsampled data in the traffic matrix. As this matrix can be approximated to be of low rank, matrix completion (MC) is an effective method to estimate the unsampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between the unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e., average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring.With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in trafficmonitoring. By utilizing the patrol algorithms for real-data-set analysis, the estimation error reduces from 35% to about 10%, compared with the random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.

  • 3.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Power Allocation for Channel Estimation and EnergyBeamforming in Wirelessly Powered Sensor Networks2018In: Proceedings of IEEE International Conference on Communications Workshops, 2018Conference paper (Refereed)
    Abstract [en]

    Wirelessly powered sensor networks (WPSNs) are becoming increasingly important to monitor many internet-of-things systems. In these WPSNs, dedicated base stations (BSs) with multiple antennas charge the sensor nodes without the need of replacing their batteries thanks to two essential procedures: i)  getting of the channel state information of the nodes by sending pilots, and based on this, ii) performing energy beamforming to transmit energy to the nodes. However, the BSs have limited power budget and thus these two procedures are not independent, contrarily to what  is assumed in some previous studies. In this paper, we investigate the novel problem of how to optimally allocate the power for channel estimation and energy transmission. Although the problem is non-convex, we provide a new solution approach and a performance analysis in terms of optimality and complexity. We also provide a closed form solution for the case where the channels are estimated based on a least square estimation. The simulations show a gain of approximately 10% in allocating the power optimally, and the importance of improving the channel estimation efficiency.

  • 4.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Flowing with the water: On optimal monitoring of water distribution networks by mobile sensors2016Conference paper (Refereed)
  • 5.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Joint node deployment and wireless energy transfer scheduling for immortal sensor networks2017In: 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7959918Conference paper (Refereed)
    Abstract [en]

    The lifetime of a wireless sensor network (WSN) is limited by the lifetime of the individual sensor nodes. A promising technique to extend the lifetime of the nodes is wireless energy transfer. The WSN lifetime can also be extended by exploiting the redundancy in the nodes' deployment, which allows the implementation of duty-cycling mechanisms. In this paper, the joint problem of optimal sensor node deployment and WET scheduling is investigated. Such a problem is formulated as an integer optimization whose solution is challenging due to the binary decision variables and non-linear constraints. To solve the problem, an approach based on two steps is proposed. First, the necessary condition for which the WSN is immortal is established. Based on this result, an algorithm to solve the node deployment problem is developed. Then, the optimal WET scheduling is given by a scheduling algorithm. The WSN is shown to be immortal from a networking point of view, given the optimal deployment and WET scheduling. Theoretical results show that the proposed algorithm achieves the optimal node deployment in terms of the number of deployed nodes. In the simulation, it is shown that the proposed algorithm reduces significantly the number of nodes to deploy compared to a random-based approach. The results also suggest that, under such deployment, the optimal scheduling and WET can make WSNs immortal.

  • 6.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Lifetime Maximization for Sensor Networks with Wireless Energy Transfer2016In: 2016 IEEE International Conference on Communications, ICC 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 20-25, article id 7510602Conference paper (Refereed)
    Abstract [en]

    In Wireless Sensor Networks (WSNs), to supply energy to the sensor nodes, wireless energy transfer (WET) is a promising technique. One of the most efficient procedures to transfer energy to the sensor nodes consists in using a sharp wireless energy beam from the base station to each node at a time. A natural fundamental question is what is the lifetime ensured by WET and how to maximize the network lifetime by scheduling the transmissions of the energy beams. In this paper, such a question is addressed by posing a new lifetime maximization problem for WET enabled WSNs. The binary nature of the energy transmission process introduces a binary constraint in the optimization problem, which makes challenging the investigation of the fundamental properties of WET and the computation of the optimal solution. The sufficient condition for which the WET makes WSNs immortal is established as function of the WET parameters. When such a condition is not met, a solution algorithm to the maximum lifetime problem is proposed. The numerical results show that the lifetime achieved by the proposed algorithm increases by about 50% compared to the case without WET, for a WSN with a small to medium size number of nodes. This suggests that it is desirable to schedule WET to prolong lifetime of WSNs having small or medium network sizes.

  • 7.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Poster: On the Immortality of Wireless Sensor Networks by Wireless Energy Transfer - A Node Deployment Perspective2017In: Proceedings of International Conference on Embedded Wireless Systems and Networks, 2017Conference paper (Refereed)
    Abstract [en]

    The lifetime of wireless sensor networks (WSNs) can be substantially extended by transferring energy wirelessly to the sensor nodes. In this poster, a wireless energy transfer (WET) enabled WSN is presented, where a base station transfers energy wirelessly to the sensor nodes that are deployed in several regions of interest, to supply them with energy to sense and to upload data. The WSN lifetime can be extended by deploying redundant sensor nodes, which allows the implementation of duty-cycling mechanisms to reduce nodes' energy consumption. In this context, a problem on sensor node deployment naturally arises, where one needs to determine how many sensor nodes to deploy in each region such that the total number of nodes is minimized, and the WSN is immortal. The problem is formulated as an integer optimization, whose solution is challenging due to the binary decision variables and a non-linear constraint. A greedy-based algorithm is proposed to achieve the optimal solution of such deployment problem. It is argued  that such scheme can be used in monitoring systems in smart cities, such as smart buildings and water lines.

  • 8.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Xiag, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory.
    Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing2015In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 33, no 12, p. 2997-3010Article in journal (Refereed)
    Abstract [en]

    The recent development of low cost wireless sensors enables novel internet-of-things (IoT) applications, such as the monitoring of water distribution networks. In such scenarios, the lifetime of the wireless sensor network (WSN) is a major concern, given that sensor node replacement is generally inconvenient and costly. In this paper, a compressive sensing-based scheduling scheme is proposed that conserves energy by activating only a small subset of sensor nodes in each timeslot to sense and transmit. Compressive sensing introduces a cardinality constraint that makes the scheduling optimization problem particularly challenging. Taking advantage of the network topology imposed by the IoT water monitoring scenario, the scheduling problem is decomposed into simpler subproblems, and a dynamic-programming-based solution method is proposed. Based on the proposed method, a solution algorithm is derived, whose complexity and energy-wise performance are investigated. The complexity of the proposed algorithm is characterized and its performance is evaluated numerically via an IoT emulator of water distribution networks. The analytical and numerical results show that the proposed algorithm outperforms state-of-the-art approaches in terms of energy consumption, network lifetime, and robustness to sensor node failures. It is argued that the derived solution approach is general and it can be potentially applied to more IoT scenarios such as WSN scheduling in smart cities and intelligent transport systems.

  • 9.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES).
    Gkatzikis, Lazaros
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Xiao, Ming
    A Sensor  Scheduling  Protocol for  Energy-efficiency and  Robustness to Failures2016In: Multimedia Communications Technical Committee Communications, Vol. 11, no 3, p. 10-14Article in journal (Refereed)
  • 10.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Energy efficient monitoring of water distribution networks via compressive sensing2015In: 2015 IEEE International Conference on Communications (ICC), IEEE conference proceedings, 2015, Vol. 2015, p. 6681-6686Conference paper (Refereed)
    Abstract [en]

    The recent development of low cost wireless sensors enables water monitoring through dense wireless sensor networks (WSN). Sensor nodes are battery powered devices, and hence their limited energy resources have to be optimally managed. The latest advancements in compressive sensing (CS) provide ample promise to increase WSNs lifetime by limiting the amount of measurements that have to be collected. Additional energy savings can be achieved through CS-based scheduling schemes that activate only a limited number of sensors to sense and transmit their measurements, whereas the rest are turned off. The ultimate objective is to maximize network lifetime without sacrificing network connectivity and monitoring performance. This problem can be approximated by an energy balancing approach that consists of multiple simpler subproblems, each of which corresponds to a specific time period. Then, the sensors that should be activated within a given period can be optimally derived through dynamic programming. The complexity of the proposed CS-based scheduling scheme is characterized and numerical evaluation reveals that it achieves comparable monitoring performance by activating only a fraction of the sensors.

  • 11.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Gkatzikis, Lazaros
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    On Maximizing Sensor Network Lifetime by Energy Balancing2018In: IEEE Transactions on Control of Network Systems, ISSN 2325-5870, Vol. 5, no 3Article in journal (Refereed)
    Abstract [en]

    Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.

  • 12.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Ozcelikkale, A.
    Fischione, Carlo
    KTH, School of Electrical Engineering (EES), Network and Systems engineering.
    Xiao, Ming
    KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Optimal energy beamforming and data routing for immortal wireless sensor networks2017In: 2017 IEEE International Conference on Communications, ICC 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 7996326Conference paper (Refereed)
    Abstract [en]

    Wireless sensor networks (WSNs) consist of energy limited sensor nodes, which limits the network lifetime. Such a lifetime can be prolonged by employing the emerging technology of wireless energy transfer (WET). In WET systems, the sensor nodes can harvest wireless energy from wireless charger, which can use energy beamforming to improve the efficiency. In this paper, a scenario where dedicated wireless chargers with multiple antennas use energy beamforming to charge sensor nodes is considered. The energy beamforming is coupled with the energy consumption of sensor nodes in terms of data routing, which is one novelty of the paper. The energy beamforming and the data routing are jointly optimized by a non-convex optimization problem. This problem is transformed into a semidefinite optimization problem, for which strong duality is proved, and thus the optimal solution exists. It is shown that the optimal solution of the semi-definite programming problem allows to derive the optimal solution of the original problem. The analytical and numerical results show that optimal energy beamforming gives two times better monitoring performance than that of WET without using energy beamforming.

  • 13.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Ozcelikkale, Ayca
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Xiao, Ming
    Towards Immortal Wireless Sensor Networks by Optimal Energy Beamforming and Data Routing2018In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 17, no 8, p. 5338-5352Article in journal (Refereed)
    Abstract [en]

    The lifetime of a wireless sensor network (WSN) determines how long the network can be used to monitor the area of interest. Hence, it is one of the most important performance metrics for WSN. The approaches used to prolong the lifetime can be briefly divided into two categories: reducing the energy consumption, such as designing an efficient routing, and providing extra energy, such as using wireless energy transfer (WET) to charge the nodes. Contrary to the previous line of work where only one of those two aspects is considered, we investigate these two together. In particular, we consider a scenario where dedicated wireless chargers transfer energy wirelessly to sensors. The overall goal is to maximize the minimum sampling rate of the nodes while keeping the energy consumption of each node smaller than the energy it receives. This is done by properly designing the routing of the sensors and the WET strategy of the chargers. Although such a joint routing and energy beamforming problem is non-convex, we show that it can be transformed into a semi-definite optimization problem (SDP). We then prove that the strong duality of the SDP problem holds, and hence the optimal solution of the SDP problem is attained. Accordingly, the optimal solution for the original problem is achieved by a simple transformation. We also propose a low-complexity approach based on pre-determined beamforming directions. Moreover, based on the alternating direction method of multipliers (ADMM), the distributed implementations of the proposed approaches are studied. The simulation results illustrate the significant performance improvement achieved by the proposed methods. In particular, the proposed energy beamforming scheme significantly out-performs the schemes where one does not use energy beamforming, or one does not use optimized routing. A thorough investigation of the effect of system parameters, including the number of antennas, the number of nodes, and the number of chargers, on the system performance is provided. The promising convergence behaviour of the proposed distributed approaches is illustrated.

  • 14.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    Santi, Paolo
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Vasilakos, Athanasios
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
    The sensable city: A survey on the deployment and management for smart city monitoringIn: IEEE Communications Surveys and Tutorials, ISSN 1553-877X, E-ISSN 1553-877XArticle in journal (Refereed)
    Abstract [en]

    In last two decades, various monitoring systems have been designed and deployed in urban environments, toward the realization of the so called smart cities. Such systems are based on both dedicated sensor nodes, and ubiquitous but not dedicated devices such as smart phones and vehicles' sensors. When we design sensor network monitoring systems for smart cities, we have two essential problems: node deployment and sensing management. These design problems are challenging, due to large urban areas to monitor, constrained locations for deployments, and heterogeneous type of sensing devices. There is a vast body of literature from different disciplines that have addressed these challenges. However, we do not have yet a comprehensive understanding and sound design guidelines. This paper addresses such a research gap and provides an overview of the theoretical problems we face, and what possible approaches we may use to solve these problems. Specifically, this paper focuses on the problems on both the deployment of the devices (which is the system design/configuration part) and the sensing management of the devices (which is the system running part). We also discuss how to choose the existing algorithms in different type of monitoring applications in smart cities, such as structural health monitoring, water pipeline networks, traffic monitoring. We finally discuss future research opportunities and open challenges for smart city monitoring.

  • 15.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Shokri-Ghadikolaei, Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Wirelessly-powered Sensor Networks: Power Allocation for Channel Estimation and Energy beamformingIn: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248Article in journal (Refereed)
    Abstract [en]

    Wirelessly-powered sensor networks (WPSNs) are becoming increasingly important to monitor many internet-of-things systems. We consider a WPSN where a multiple-antenna base station, dedicated for energy transmission, sends pilot signals to estimate the channel state information and consequently shapes the energy beams toward the sensor nodes. Given a fixed energy budget at the base station, in this paper, we investigate the novel problem of optimally allocating the power for the channel estimation and for the energy transmission. We formulate this problem for general channel estimation and beamforming schemes, which turns out to be non-convex. We provide a new solution approach and a performance analysis in terms of optimality and complexity. We also present a closed-form solution for the case where the channels are estimated based on a least square channel estimation and a maximum ratio transmit beamforming scheme. The analysis and simulations indicate a significant gain in terms of the network sensing rate, compared to the fixed power allocation, and the importance of improving the channel estimation efficiency.

  • 16.
    Du, Rong
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Xiao, Ming
    KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems engineering.
    Optimal Node Deployment and Energy Provision for Wirelessly Powered Sensor Networks2018In: IEEE Journal on Selected Areas in Communications, ISSN 0733-8716, E-ISSN 1558-0008, Vol. 37, no 2, p. 407-423Article in journal (Refereed)
    Abstract [en]

    In a typical wirelessly powered sensor network (WPSN), wireless chargers provide energy to sensor nodes by using wireless energy transfer (WET). The chargers can greatly improve the lifetime of a WPSN using energy beamforming by a proper charging scheduling of energy beams. However, the supplied energy still may not meet the demand of the energy of the sensor nodes. This issue can be alleviated by deploying redundant sensor nodes, which not only increase the total harvested energy, but also decrease the energy consumption per node provided that an efficient  scheduling of the sleep/awake of the nodes is performed. Such a problem of joint optimal sensor deployment, WET scheduling, and node activation is posed and investigated in this paper. The problem is an integer optimization that is challenging due to the binary decision variables and non-linear constraints. Based on the analysis of the necessary condition such that the WPSN be immortal, we decouple the original problem into a node deployment problem and a charging and activation scheduling problem. Then, we propose an algorithm and prove that it achieves the optimal solution under a mild condition. The simulation results show that the proposed algorithm reduces the needed nodes to deploy by approximately 16%, compared to a random-based approach. The simulation also shows if the battery buffers are large enough, the optimality condition will be easy to meet.

  • 17.
    Rong, Du
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Wireless Sensor Networks in Smart Cities: The Monitoring of Water Distribution Networks Case2016Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The development of wireless sensor networks (WSNs) is making it possible to monitor our cities. Due to the small size of the sensor nodes, and their capabilities of transmitting data remotely, they can be deployed at locations that are not easy or impossible to access, such as the pipelines of water distribution networks (WDNs), which plays an important role in protecting environment and securing public health.

      The design of WSNs for WDNs faces major challenges. Generally, WSNs are resource-limited because most of the sensor nodes are battery powered. Thus, their resource allocation has to be carefully controlled. The thesis considers two prominent problems that occur when designing WSNs for WDNs: scheduling the sensing of the nodes of static WSNs, and sensor placement for mobile WSNs. These studies are reported in the thesis from three published or submitted papers. In the first paper, the scheduling of sleep/sensing for each sensor node is considered to maximize the whole WSNs lifetime while guaranteeing a monitoring performance constraint. The problem is transformed into an energy balancing problem, and solved by a dynamic programming based algorithm. It is proved that this algorithm finds one of the optimal solutions for the energy balancing problem. In the second paper, the question of how the energy balancing problem approximates the original scheduling problem is addressed. It is shown that even though these two problems are not equivalent, the gap of them is small enough. Thus, the proposed algorithm for the energy balancing problem can find a good approximation solution for the original scheduling problem. The second part of the thesis considers the use of mobile sensor nodes. Here, the limited resource is the number of available such mobile nodes. To maximize the monitoring coverage in terms of population, an optimization problem for determining the releasing locations for the mobile sensor nodes is formulated. An approximate solution algorithm based on submodular maximization is proposed and its performance is investigated. Beside WDNs, WSN applications for smart cities share a common characteristic: the area to monitor usually has a network structure. Therefore, the studies of this thesis can be potentially generalized for several IoT scenarios.

  • 18.
    Zeng, Ming
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Du, Rong
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fodor, Viktória
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Fischione, Carlo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.
    Computation Rate Maximization for Wireless Powered Mobile Edge Computing with NOMA2019In: Proceedings 20th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (IEEE WoWMoM 2019), IEEE , 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider a mobile edge computing (MEC) network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC as a whole. For the offloading users, non-orthogonal multiple access (NOMA) is adopted for information transmission. We consider rate-adaptive computational tasks and aim at maximizing the sum computation rate of all users by jointly optimizing the individual computing mode selection (local computing or offloading), the time allocations for energy transfer and for information transmission, together with the local computing speed or the transmission power level. The major difficulty of the rate maximization problem lies in the combinatorial nature of the multiuser computing mode selection and its involved coupling with the time allocation. We also study the case where the offloading users adopt time division multiple access (TDMA) as a benchmark, and derive the optimal time sharing among the users. We show that the maximum achievable rate is the same for the TDMA and the NOMA system, and in the case of NOMA it is independent from the decoding order, which can be exploited to improve system fairness. To maximize the sum computation rate, for the mode selection we propose a greedy solution based on the wireless channel gains, combined with the optimal allocation of energy transfer time. Numerical results show that the proposed solution maximizes the computation rate in homogeneous networks, and binary offloading leads to significant gains. Moreover, NOMA increases the fairness of rate distribution among the users significantly, when compared with TDMA.

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