ACTP: An Advanced Data Collection Protocol based on CTP

Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented protocol uses a novel path with a communication time-slot assignment algorithm to reduce the count of cycles that are needed for the data collection process with reduction of 50% of the number of cycles needed for other protocols. Therefore, the time and energy needed for data collection are reduced by approximately 25%and 6% respectively, which prolongs the network lifetime. The proposed protocol is called Energy-Time Aware Data Collection Protocol (ETCL).

Smartphone-based data collection from wireless sensor networks in an urban environment

Vehicular sensor networks (VSNs) have emerged as a new technology, which combines wireless communication offered by vehicular ad hoc NETworks with sensing devices installed in vehicles or in roadsides. VSN creates a huge opportunity to extend the roadside sensor infrastructure of existing traffic control systems. The main issues in VSNs are inherent in the data collection and its dissemination in such a large-scale network, characterized by frequent topology changes and network partitioning. This paper proposes a new multi-hop data collection and dissemination protocol based on data replication approach. The aim is an efficient collection and dissemination of data from a defined geographic area. Differently from other data collection protocols for VSNs, which have for purpose to collect data for traffic control applications or monitoring applications, our proposed protocol called Collaborative Data Collection Protocol (CDCP for VSNs) focuses on collecting data to be used in non-delay tolerant applications, so CDCP aims to collect the requested data within an optimized delay, but also, focuses in making this data accessible for other potential requests. Simulation results show the effectiveness of our proposed protocol.

To satisfy the increasing demand of wireless sensor network (WSN) distribution in the different monitoring area and enlarge the coverage of the WSNs in various scenes of application, in this paper, we constructed a new type of mobile wireless data sinking platform for collecting the data of WSN based on the technology of unmanned aerial vehicle (UAV). First, the authors studied a wireless environmental monitoring system containing the UAV platform and WSN is constructed and both test on wireless communication capacity and data collecting are carried out. The communication capacity test show that when the RF modules are operated with transmission power greater than 1 dBm and communication distance less than 100 m, the UAV wireless sink node can maintain a high quality communication data link which has a lower PLR (less than the 5 %) and better signal level (RSSI greater than −90 dBm) with transmission data rate of 250 kbps, using this RF transceiver a simple communication & data collection protocol between UAV and ground WSN sink node was proposed. The whole work of this paper should provide some experience for building a UAV cooperative WSN system in the future.

Fast and energy efficient data collection is the most important function of wireless sensor networks (WSNs) which governs the performance of WSNs. Time division multiple access (TDMA) is an effective data collection strategy in which node only schedules the active slot to save energy when transmitting data. However, many previous studies are designed for static traffic pattern such that each node to generate one data packet in a data collection cycle. Moreover, all nodes in the network use the same transmission radius r, so that the network data collection has a larger delay, a lower lifetime, and energy utilization rate. In view of the shortcomings of previous research, this paper proposes a reducing delay and maximizing lifetime (RDML) data collection strategy for WSNs with dynamic traffic patterns. The main innovation point of our work is the data transmission radius is no longer unified as r, while some nodes with energy surplus use a data transmission radius of mr(m > 1). The following network performance can be achieved by adopting the RDML scheme. First, because the nodes in the near sink mr radius area can send data directly to sink, the amount of data carried by hotspots nodes in the original strategy is greatly reduced, and the lifetime is improved. Second, since some nodes can go mr distance to the sink in data transmission, thus accelerating the data routing and reducing the time needed for data collection. Finally, since the remaining energy of nodes is fully utilized to increase the radius of data transmission, the energy utilization rate can be effectively improved. Through the theoretical analysis and experimental results, the RDML scheme can reduce the maximum delay of data collection by 22.32% and improve lifetime as well as the energy utilization rate by 0.86%-84.40%, and 23.33%-88.00%, respectively, compared with previous strategies.

Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other's sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement.

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.KeywordsSensor NetworkSensor NodeWireless Sensor NetworkSteiner TreeSensor ReadingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Technological advances have made Wireless Sensor Networks (WSN) more reliable, low cost, and widely used in a variety of applications. As WSN nodes are low power battery operated with limited lifespan, optimizing their resources (sensing, channel use, computing) is an essential task for the success of applications implemented over these networks. Even though this task could be challenging for large WSN, its impact in terms of network survivability and economic benefits can be significant. In this paper, we will focus on optimizing radio data transmission and evaluate its impact on power saving. We will analyze the data collected from a large WSN, more than 20,000 nodes, used for water meter readings in the City of Moncton. Two data driven approaches, reduction and prediction-based, are evaluated and compared. Then we propose a system for data collection and transmission that improves power consumption, in comparison with the existing one, without affecting the effectiveness of the application in terms of water consumption monitoring and detection of leaks. Experimental results show substantial power savings and significant increases in battery lifespan.

This paper considers the downlink of a cache- enabled fog radio access network (F-RAN) with limited fronthaul capacity, where user association (UA), data delivery rate (DDR) and signal precoding are jointly optimized. We formulate a mixed-integer nonlinear programming problem in which the weighted difference of network throughput and total power consumption is maximized, subject to the predefined DDR requirements and the maximum transmit power at each eRRH. To address this challenging problem, we first apply the l0-norm approximation and l1-norm minimization techniques to deal with the UA. After this key step, we arrive at an approximated problem that only involves the joint optimization of DDR and precoding. By using the alternating descent method, we further decompose this problem into a convex subproblem for DDR allocation and a nonconvex subproblem for precoding design. While the former is globally solved by the interior-point method, the latter is solved by a specifically tailored successive convex quadratic programming method. Finally, we propose an iterative algorithm for the original joint optimization that is guaranteed to converge. Importantly, each iteration of the developed algorithm only involves solving simple convex problems. Numerical examples demonstrate that the proposed design significantly improves both throughput and power performances, especially in practical F-RANs with limited fronthaul capacity. Compared to the sole precoder design for a given cache placement, our joint design is shown to improve the throughput by 50% while saving at least half of the total power consumption in the considered examples.

Wireless sensor networks (WSN) are widely used in various situations. Energy saving is one of the most important issues because of the limited power. Communication is the mainly energy consuming part of sensor nodes. Reducing the size of transmitted data can conserve the energy of nodes. Spatial and temporal correlation is ubiquitous in wireless sensor networks. By exploiting spatial and temporal correlation, only a subset of data need to be transmitted and the rest of the data can be estimated. Data aware clustering is an effective way to exploit spatial correlation among sensor nodes. In our SC-EEDC framework, fuzzy ART artificial neural network is used to measure the shape similarity among data sequences and the magnitude similarity is estimated by a magnitude similarity model. And two corresponding estimation algorithm are proposed. We propose Weighting based K-means clustering algorithm (WK-means), which considers multiple clustering factors in addition to the data similarity. The K-means algorithm structure is introduced in clustering algorithm to search the more energy-saving clustering topology. Anchor node based data collection strategy is proposed to measure the spatial correlation in real time and suppress the transmission of spatial redundant data at source node. Sleeping scheduling is introduced to dynamically adjust the spatial sampling rate and the temporal redundancy is reduced using Length Encoding. The cluster maintenance scheme keeps the cluster structure’s efficiency over the network lifetime. Simulation results show that our SC-EEDC achieves significant data reduction without affecting the accuracy of collected data, reduces the energy consumption in each round of data collection and effectively prolongs the network lifetime.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Data collection is a fundamental task in wireless sensor networks. In many applications of wireless sensor networks, approximate data collection is a wise choice due to the constraints in communication bandwidth and energy budget. In this paper, we focus on efficient approximate data collection with prespecified error bounds in wireless sensor networks. The key idea of our data collection approach ADC (Approximate Data Collection) is to divide a sensor network into clusters, discover local data correlations on each cluster head, and perform global approximate data collection on the sink node according to model parameters uploaded by cluster heads. Specifically, we propose a local estimation model to approximate the readings of sensor nodes in subsets, and prove rated error-bounds of data collection using this model. In the process of model-based data collection, we formulate the problem of selecting the minimum subset of sensor nodes into a minimum dominating set problem which is known to be NP-hard, and propose a greedy heuristic algorithm to find an approximate solution. We further propose a monitoring algorithm to adaptively adjust the composition of node subsets according to changes of sensor readings. Our trace-driven simulations demonstrate that ADC remarkably reduces communication cost of data collection with guaranteed error bounds.

Considering a wireless sensor network with optimal mobile sink track along which data are transmitted to the nodes, a fraction of nodes in the area of interest generate data packets and send them to the sink. Usually the single path routing is adopted in such scenarios which may cause unnecessary transmissions. We address the problem of how to collect these packets efficiently by using network coding technique. Network coding is a kind of in network computation which has many advantages over traditional routing, such as providing higher network throughput, using bandwidth efficiently and balancing the traffic. In this paper, we propose a data collection strategy by using network coding for efficient data collection in event driven WSNs and explore its performance through theoretical analysis. We present a strategy for network coding algorithm which can guarantee the successful decoding for all the encoded packets. Specifically, our numerical results show the decrease in the total number of transmissions by applying network coding than that by using the single path routing. Also numerical results indicate that the delivery packets rate is significantly improved as the increase of the link loss rate comparing network coding we adopt with single path routing.

Wireless ad hoc routing protocols must employ accurate and rapid link failure detection mechanisms in order to maintain valid multihop routes and provide high data delivery rates. Hello-based failure detection is the predominant failure detection mechanism due to its ease of implementation. Despite its prevalence, no analytical work has been carried out to better understand its fundamental behavior. In this paper, we study the performance of hello-based link failure detection via analysis and experimentation. Our analytical results, which are validated by experimental results, show the existence of optimal hello beaconing parameters which depend on network conditions such as traffic load, link failure rate and hello delivery rate. These results can be applied in real-world network deployments for obtaining the optimal hello beaconing parameters that can provide the highest data delivery rate.

Wireless sensor networks (WSNs) are among the most promising technologies of the new millennium. The opportunities afforded by being able to program networks of small, lightweight, low-power, computation- and bandwidth-limited nodes have attracted a large community of researchers and developers. However, the unique set of capabilities offered by the technology produces an exciting but complex design space, which is often difficult to negotiate in an application context. Deploying sensing physical environments produces its own set of challenges, and can push systems into failure modes, thus revealing problems that can be difficult to discover or reproduce in simulation or the laboratory.Sustained efforts in the area of wireless networked sensing over the last 15 years have resulted in a large number of theoretical developments, substantial practical achievements, and a wealth of lessons for the future. It is clear that in order to bridge the gap between (on the one hand) visions of very large scale, autonomous, randomly deployed networks and (on the other) the actual performance of fielded systems, we need to view deployment as an essential component in the process of developing sensor networks: a process that includes hardware and software solutions that serve specific applications and end-user needs. Incorporating deployment into the design process reveals a new and different set of requirements and considerations, whose solutions require innovative thinking, multidisciplinary teams and strong involvement from end-user communities.This special feature uncovers and documents some of the hurdles encountered and solutions offered by experimental scientists when deploying and evaluating wireless sensor networks in situ, in a variety of well specified application scenarios.The papers specifically address issues of generic importance for WSN system designers: (i) data quality, (ii) communications availability and quality, (iii) alternative, low-energy sensing modalities and (iv) system solutions with high end-user added value and cost benefits. The common thread is deployment and deployment evaluation. In particular, satisfaction of application requirements, involvement of the end-user in the design and deployment process, satisfactory system performance and user acceptance are concerns addressed in many of the contributions. The contributions form a valuable set, which help to identify the priorities for research in this burgeoning area: Robust, reliable and efficient data collection in embedded wireless multi-hop networks are essential elements in creating a true deploy-and-forget user experience. Maintaining full connectivity within a WSN, in a real world environment populated by other WSNs, WiFi networks or Bluetooth devices that constitute sources of interference is a key element in any application, but more so for those that are safety-critical, such as disaster response. Awareness of the effects of wireless channel, physical position and line-of-sight on received signal strength in real-world, outdoor environments will shape the design of many outdoor applications. Thus, the quantification of such effects is valuable knowledge for designers. Sensors' failure detection, scalability and commercialization are common challenges in many long-term monitoring applications; transferable solutions are evidenced here in the context of pollutant detection and water quality. Innovative, alternative thinking is often needed to achieve the desired long-lived networks when power-hungry sensors are foreseen components; in some instances, the very problems of wireless technology, such as RF irregularity, can be transformed into advantages. The importance of an iterative design and evaluation methodology—from analysis to simulation to real-life deployment—should be well understood by all WSN developers. The value of this is highlighted in the context of a challenging WPAN video-surveillance application based on a novel Nomadic Access Mechanism. Cost benefits to be drawn from devising a WSN based solution to classic application areas such as surveillance are often a prime motivator for WSN designers; an example is offered here based on the use of intelligent agents for intrusion monitoring. Last but not least, the practicality and usability of the WSN solutions found for novel applications is key to their adoption. This is particularly true when the end-users of the developed technology are medical patients. The importance of feedback, elegant hardware encapsulation and extraction of meaning from data is presented in the context of novel orthopedic rehabilitation aids.Overall, this feature offers wide coverage of most issues encountered in the process of design, implementation and evaluation of deployable WSN systems. We trust that designers and developers of WSN systems will find much work of value, ranging from lessons learned, through solutions to known hurdles, to novel developments that enhance applications. Finally, we would like to thank all authors for their valuable contributions!