Dense Wireless Sensor Networks Research Articles

Gap-filling snow-depth time-series with Kalman Filtering-Smoothing and Expectation Maximization: Proof of concept using spatially dense wireless-sensor-network data

We assessed the performance of Kalman Filtering-Smoothing and Expectation Maximization (EM-KF) in gap filling snow-depth sensor data. To this end, hourly snow-depth data from three spatially dense wireless-sensor networks were randomly removed and imputed using EM-KF. Maximum gap size was 40+ hours and differences between artificially removed and gap-filled data larger than 20 cm were removed before computing Root Mean Square Error and Bias. These differences are spurious over- and underestimations that can generally be identified through visual inspection, likely due to instability in the gap-filling process. The expected accuracy of EM-KF in this initial, controlled proof of concept is close to measurement uncertainty (1 to 2 cm for ultrasonic depth sensors). Compared to regressing missing data against nearby sensors, a frequently used strategy in the field, EM-KF tends to yield smaller errors in networks with a comparatively large number of co-located sensors (nine and eight as opposed to four in a third network). In these data-rich networks, maximum differences in daily Root Mean Square Errors between EM-KF and a regression are up to 6 to 8 cm at a daily time scale, with peaks in winter and in particular during snowfalls. EM-KF yields superior results particularly during snowfalls, likely because it exploits the temporal structure and uncertainty in the data through a state-space model. In the third network with fewer co-located sensors, differences in accuracy between EM-KF and a multilinear regression were inconsistent. This implies that the performance of EM-KF benefits from increasing the amount of available information and with increasing dependency of data across nodes. Temporally and spatially dense snow-depth data are being increasingly collected in operational contexts: EM-KF may support supervised filling of gaps in these data – particularly during snowfall events – and thus provide continuous-time information for avalanche or water-resources forecasting in snow-dominated regions. Automatic, unsupervised gap-filling using EM-KF will necessarily need more research to identify reasons of spurious over- and underestimations. Future work should also upscale this proof of concept to operational sensor networks spanning large water basins to bring conclusions closer to real-world applications.

Hardware Efficient Solutions for Wireless Air Pollution Sensors Dedicated to Dense Urban Areas

This paper proposes novel solutions for the application of air pollution monitoring systems in so called ‘smart cities’. A possibility of the implementation of a relatively dense network of wireless air pollution sensors that can collect and process data in real time was the motive behind our research and investigations. We discuss the concept of the wireless sensor network, taking into account the structure of the urban development in cities and we present a novel signal processing algorithm that may be used to control the communication scheme between particular sensors and an external network. We placed a special emphasis on the computational complexity to facilitate the implementation directly at the transistor level of particular sensors. The algorithm was verified using real data obtained from air pollution sensors installed in Krakow, Poland. To ensure sufficient robustness of the variability of input data, we artificially added high amplitude noise to the real data we obtained. This paper demonstrates the performance of the algorithm. This algorithm allows for the reduction of the noise amplitude by 23 dB and enables a reduction of the number of wireless communication sessions with a base station (BS) by 70%–80%. We also present selected measurement results of a prototype current-mode digital-to-analogue converter to be used in the sensors, for signal resolutions up to 7 bits.

A green cluster‐based routing scheme for large‐scale wireless sensor networks

SummaryIn wireless sensor networks (WSNs), clustering has been shown to be an efficient technique to improve scalability and network lifetime. In clustered networks, clustering creates unequal load distribution among cluster heads (CHs) and cluster member (CM) nodes. As a result, the entire network is subject to premature death because of the deficient active nodes within the network. In this paper, we present clustering‐based routing algorithms that can balance out the trade‐off between load distribution and network lifetime “green cluster‐based routing scheme.” This paper proposes a new energy‐aware green cluster‐based routing algorithm to preventing premature death of large‐scale dense WSNs. To deal with the uncertainty present in network information, a fuzzy rule‐based node classification model is proposed for clustering. Its primary benefits are flexibility in selecting effective CHs, reliability in distributing CHs overload among the other nodes, and reducing communication overhead and cluster formation time in highly dense areas. In addition, we propose a routing scheme that balances the load among sensors. The proposed scheme is evaluated through simulations to compare our scheme with the existing algorithms available in the literature. The numerical results show the relevance and improved efficiency of our scheme.

Tone-Based Contention Resolution for Multi-hop Wireless Sensor Networks

Medium access control protocols with common active period (AP MACs) are frequently used in wireless sensor networks (WSNs) with medium-load traffic requirements. In this class of low duty-cycle contention-based protocols, sensor nodes synchronously alternate between active periods (used for data exchange) and sleep periods (used for energy conservation). The key component of AP MAC protocols is the contention resolution mechanism (CRM), which should provide channel access to as many nodes as possible, while eliminating, or at least minimizing the chance of date message collisions. Current AP MAC protocols commonly employ CRMs based on the traditional CSMA scheme, possibly extended with RTS–CTS handshake mechanism, which are known to be energy inefficient under heavy traffic conditions, especially in dense multi-hop WSNs. In this paper, we propose a collision-free and energy-efficient CRM, specially designed for application in AP MACs. The proposed CRM, referred to as AP/TONE protocol, replaces RTS-CTS control packets with short RF tones for exchanging elementary status information among neighbouring nodes. At the top of the tone-based signalling mechanism, AP/TONE implements a distributed two-phase multi-round contention resolution procedure. In the first phase, the network is partitioned into non-interfering clusters by selectively blocking sending/receiving status of contending nodes. In the second phase, the existing intra-cluster tone-based CRM is used to make the final selection. The performance of the proposed approach was evaluated by simulation of a multi-hop WSN with varying traffic parameters and node density. The obtained results show that AP/TONE achieves significantly higher throughput with reduced energy consumption with respect to CSMA-based approaches.

Random Neural Networks with Hierarchical Committees for Improved Routing in Wireless Mesh Networks with Interference

We propose a hierarchical (nested) variant of a recurrent random neural network (RNN) with reinforced learning, introduced by Gelenbe. Each neuron (committee) in a top-level RNN represents a different bottom-level RNN (or sub-committee). The bottom-level RNNs choose the best routing and the top-level RNN chooses the currently best bottom-level RNN. Each of the bottom RNNs is trained in a different way. When they differ in their choice of the best path, several cognitive packets are routed according to the different decisions. In that case, a respective ACK packet trains individual bottom RNNs and not all bottom RNNs at once. An example presents an optimisation of a real-time routing in a dense mesh network of wireless sensors relaying small metering messages between each other, until the messages reach a common gateway. The network is experiencing a periodic electromagnetic interference. The hierarchical variant causes a small increase in the number of smart packets but allows a considerably better routing quality.

Performance Evaluation and Interference Characterization of Wireless Sensor Networks for Complex High-Node Density Scenarios.

The uncontainable future development of smart regions, as a set of smart cities’ networks assembled, is directly associated with a growing demand of full interactive and connected ubiquitous smart environments. To achieve this global connection goal, large numbers of transceivers and multiple wireless systems will be involved to provide user services and applications anytime and anyplace, regardless the devices, networks, or systems they use. Adequate, efficient and effective radio wave propagation tools, methodologies, and analyses in complex indoor and outdoor environments are crucially required to prevent communication limitations such as coverage, capacity, speed, or channel interferences due to high-node density or channel restrictions. In this work, radio wave propagation characterization in an urban indoor and outdoor wireless sensor network environment has been assessed, at ISM 2.4 GHz and 5 GHz frequency bands. The selected scenario is an auditorium placed in an open free city area surrounded by inhomogeneous vegetation. User density within the scenario, in terms of inherent transceivers density, poses challenges in overall system operation, given by multiple node operation which increases overall interference levels. By means of an in-house developed 3D ray launching (3D-RL) algorithm with hybrid code operation, the impact of variable density wireless sensor network operation is presented, providing coverage/capacity estimations, interference estimation, device level performance and precise characterization of multipath propagation components in terms of received power levels and time domain characteristics. This analysis and the proposed simulation methodology, can lead in an adequate interference characterization extensible to a wide range of scenarios, considering conventional transceivers as well as wearables, which provide suitable information for the overall network performance in crowded indoor and outdoor complex heterogeneous environments.

UAV-Aided Projection-Based Compressive Data Gathering in Wireless Sensor Networks

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets. Among the emerging markets, Internet of Things (IoT) use cases are standing out with the proliferation of a wide range of sensors that can be configured to continuously monitor and transmit data for intelligent processing and decision making. Devices in such scenarios are normally extremely energy-constrained and often exist in large numbers and can be located in hard-to-reach areas; the fact that necessitates the design and implementation of effective energy-aware data collection mechanisms. To this end, we propose the utilization of unmanned aerial vehicles (UAVs) to collect data in dense wireless sensor networks using projection-based compressive data gathering (CDG) as a novel solution methodology. CDG is utilized to aggregate data en-route from a large set of sensor nodes to selected projection nodes acting as cluster heads (CHs) in order to reduce the number of needed transmissions leading to notable energy savings and extended network lifetime. The UAV transfers the gathered data from the CHs to a remote sink node, e.g., a 5G cellular base station, which avoids the need for long range transmissions or multihop communications among the sensors. Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected CH nodes based on projection-based CDG with minimized UAV trajectory distance. We formulate a joint optimization problem and divide it into four complementary subproblems to generate close-to-optimal results with lower complexity. Moreover, we propose a set of effective algorithms to generate solutions for relatively large-scale network scenarios. We demonstrate the superiority of the proposed approach and the designed algorithms via detailed performance results with analysis, comparisons, and insights.

Cooperative and Delay Minimization Routing Schemes for Dense Underwater Wireless Sensor Networks

Symmetry in nodes operation in underwater wireless sensor networks (WSNs) is crucial so that nodes consume their energy in a balanced fashion. This prevents rapid death of nodes close to water surface and enhances network life span. Symmetry can be achieved by minimizing delay and ensuring reliable packets delivery to sea surface. It is because delay minimization and reliability are very important in underwater WSNs. Particularly, in dense underworks, packets reliability is of serious concern when a large number of nodes advance packets. The packets collide and are lost. This inefficiently consumes energy and introduces extra delay as the lost packets are usually retransmitted. This is further worsened by adaptation of long routes by packets as the network size grows, as this increases the collision probability of packets. To cope with these issues, two routing schemes are designed for dense underwater WSNs in this paper: delay minimization routing (DMR) and cooperative delay minimization routing (CoDMR). In the DMR scheme, the entire network is divided into four equal regions. The minor sink nodes are placed at center of each region, one in each of the four regions. Unlike the conventional approach, the placement of minor sink nodes in the network involves timer based operation and is independent of the geographical knowledge of the position of every minor sink. All nodes having physical distance from sink lower than the communication range are able to broadcast packets directly to the minor sink nodes, otherwise multi-hopping is used. Placement of the minor sinks in the four regions of the network avoids packets delivery to water surface through long distance multi-hopping, which minimizes delay and balances energy utilization. However, DMR is vulnerable to information reliability due to single path routing. For reliability, CoDMR scheme is designed that adds reliability to DMR using cooperative routing. In CoDMR, a node having physical distance from the sink greater than its communication range, sends the information packets by utilizing cooperation with a single relay node. The destination and the relay nodes are chosen by considering the lowest physical distance with respect to the desired minor sink node. The received packets at the destination node are merged by fixed ratio combining as a diversity technique. The physical distance computation is independent of the geographical knowledge of nodes, unlike the geographical routing protocols. This makes the proposed schemes computationally efficient. Simulation shows that DMR and CoDMR algorithms outperform the counterpart algorithms in terms of total energy cost, energy balancing, packet delivery ratio (PDR), latency, energy left in the battery and nodes depleted of battery power.

Topology Management and TSCH Scheduling for Low-Latency Convergecast in In-Vehicle WSNs

Wireless sensor networks (WSNs) are considered as a promising solution in intravehicle networking to reduce wiring and production costs. This application requires reliable and real-time data delivery, while the network is very dense. The time-slotted channel hopping (TSCH) mode of the IEEE 802.15.4 standard provides a reliable solution for low-power networks through guaranteed medium access and channel diversity. However, satisfying the stringent requirements of in-vehicle networks is challenging and demands for special consideration in network formation and TSCH scheduling. This paper targets convergecast in dense in-vehicle WSNs, in which all nodes can potentially directly reach the sink node. A cross-layer low-latency topology management and TSCH scheduling (LLTT) technique is proposed that provides a very high timeslot utilization for the TSCH schedule and minimizes communication latency. It first picks a topology for the network that increases the potential of parallel TSCH communications. Then, by using an optimized graph isomorphism algorithm, it extracts a proper match in the physical connectivity graph of the network for the selected topology. This network topology is used by a lightweight TSCH schedule generator to provide low data delivery latency. Two techniques, namely grouped retransmission and periodic aggregation, are exploited to increase the performance of the TSCH communications. The experimental results show that LLTT reduces the end-to-end communication latency compared to other approaches, while keeping the communications reliable by using dedicated links and grouped retransmissions.

Active flow control using dense wireless sensor and actuator networks

This paper describes the design of an active flow control (AFC) system for aeronautics applications based on dense wireless sensor and actuator networks (WSANs). The objective of this AFC system is to track gradients of pressure (or wall shear stress) across the surface of the fuselage of commercial aircraft. This collected information is used to activate a set of actuators that will attempt to reduce the skin drag effect produced by the separation between laminar and turbulent flows. This is expected to be translated into increased lift-off forces, higher vehicle speeds, longer ranges and reduced fuel consumption. The paper describes the architecture of the system in the context of the European research project DEWI (dependable embedded wireless infrastructure) using the concept of the DEWI Bubble and its three-tier architecture especially designed to ensure dependability and interoperability in industrial WSANs. A system-level simulator is also proposed to model each process of the AFC system and the aeronautics DEWI Bubble infrastructure, highlighting the interactions between the network simulation and the results of the computational fluid dynamics (CFD) simulation. The key element in the proposed solution is a polygonal patch of wired sensors and actuators. This patch is provided with a wireless link to a central coordinator or access point conveniently located in the aircraft to maximize coverage to a network of distributed patches. A trade-off between scalability, size of the patches, fluid speed/viscosity, sampling sensor and actuator rates in space and time, and the capacity/delay characteristic of the wireless inter-patch and the wireline intra-patch communication technologies is also here discussed. The hybrid wireless/wired sensor and actuator network achieves great flexibility, scalability, manageability, troubleshooting, and modularity as compared to a solution exclusively based on wireline or wireless components. The final details of the prototype and results in a wind tunnel test-bed are here described, demonstrating the validity of the concept and the use of wireless technologies for aeronautical applications (flexible architecture and innovative services). Future issues regarding security, safety and trustiness of the AFC system are also briefly introduced in the context of the spin-off European project SCOTT (secure connected trusted things).

A novel DEA-OR algorithm for route failure recovery in dense wireless sensor networks

Wireless sensor nodes generally have less memory and low battery life. Due to this constraint, a strong algorithm is needed which can reduce the energy consumption. Communication in wireless sensor network (WSN) depends on active number of neighboring nodes and battery power of the operating node. Factors like neighbor availability, link stability, energy and route failures directly influence network performance, for which optimization is vital. Traditional optimization techniques advise solutions that consume higher number of iterations without considering post network metrics like link stability and path cost. To address the issues of the existing energy optimization techniques, we put forward an innovative distance and energy aware optimized routing (DEA-OR) algorithm for WSNs. Considering distance as the base factor, DEA-OR algorithm gives solution for energy efficient transmission and route failure recovery. The process is of two stages: weight based neighbor selection for routing and cost-confined greedy method for route failure recovery. In this process, backtracking process of source is prevented through which overhead in neighbor selection is minimized. Our proposed algorithm minimizes energy consumption, delay and overhead thereby improvising throughput and network lifetime.

A fuzzy-based approach for energy-efficient Wi-Fi communications in dense wireless multimedia sensor networks

Wireless multimedia sensor networks can provide valuable information for many monitoring and control applications, which can process scalar data, still images, audio and video streams. However, multimedia streaming is still very challenging for those networks, since energy constraints limit the attainable bandwidth for data packet transmissions. In some cases, dense sensor networks may have many source nodes streaming at the same time and such transmission demand may not be supported by the network protocols and defined links. Actually, for networks based on IEEE 802.15.4 protocol, multimedia streaming may be unfeasible for many scenarios, even with small numbers of sensors, severely restricting the deployment of multimedia sensor nodes. However, other standards as IEEE 802.11 could provide acceptable bandwidth for multimedia streaming, although energy efficiency is not a major concern for it. In this context, this paper proposes an energy-efficient approach that provides high bandwidth with optimized energy consumption, directly benefiting wireless multimedia sensor networks. A fuzzy-based solution is defined to determine whether Wi-Fi access points should be switched off when they are underutilized, reducing energy consumption while keeping network performance at acceptable levels.

Track-sector-tree clustering scheme for dense wireless sensor networks

Wireless sensor networks (WSNs) have become essential and useful in wide variety real time applications. Since the nodes in a sensor network are limited by energy, prolonging the life time of the network is a major challenge in the design of WSN. Radio transmission requires more power and the limited energy of nodes should be conserved while communication or message passing. The effective way to accomplish this is through clustering techniques. This paper proposes a track sector tree based clustering scheme (TSTCS) which considers the network region as concentric circles with tracks and sectors. Tree structured clusters are formed and communication between sink and CH is performed with optimal energy cost. It also provides local remedy for energy suffering cluster heads by substitution technique. Extensive simulations are done and the performance of TSTCS is compared with the latest clustering algorithms for WSN.

An Efficient Mechanism Protocol for Wireless Sensor Networks by using Grids

Multilevel short-distance clustering communication is an important scheme to reduce lost data packets over the path to the sink, particularly when nodes are deployed in a dense WSN (wireless sensor network). Our proposed protocol solves the problems of single hop paths in the TDTCGE (two-dimensional technique based on center of gravity and energy) method, which addresses only single-hop problems and does not minimize distances between nodes by using multi-hop nodes with multilevel clustering grids to avoid dropped packets and to guarantee reliable paths without failures. In multilevel clustering grids, transmitted data are aggregated from lower-level grids to upper-level grids. In this paper, the proposed protocol obtains the optimal path for data transmission between cluster heads and the sink for heterogeneous WSNs. The cluster head nodes play an important role in forwarding data originating from other normal nodes that aggregate data to upper clusterheads. This routing approach is more efficient than other routing approaches, and it provides a reliable protocol for avoidance of data loss. In addition, the proposed protocol produces sleep and wakeup signals to the nodes and cluster heads via an MD (mediation device), thereby reducing energy consumption. Simulation results demonstrate the efficiency of the proposed method in terms of fewer dropped packets and high energy efficiency. The network environment overcomes the drawbacks of failure paths and provides reliable transmission to the sink.

Weaving the Wireless Web: Toward a Low-Power, Dense Wireless Sensor Network for the Industrial IoT

Wireless sensor networks (WSNs) combine sensing, computation, and communication for automated data collection, processing, and telemetry. WSNs use spatially distributed devices for applications in which scalable connectivity and energy efficiency are increasingly important. Thus, WSNs are a key technology for the Internet of Things (IoT), whether consumer and industrial, connecting billions of devices over the Internet for applications including health care, transportation, process analysis, and environmental assessment, to name a few. However, wireless connectivity comes at the expense of power.

Efficient coding techniques algorithm for cluster-heads communication in wireless sensor networks

Random network coding (RNC) for Error Correction have been the subject of intense research lately. However, in wireless sensor networks (WSNs) the correlation between the received symbols, from the same cluster head (CH), provoke the column correlation on the generator matrix of array Low-Density Parity-Check (LDPC) codes, whereby many columns of this matrix do not offer any additional information, thus the most important properties are lost despite the fact that the generator matrices are more easily dispersed. In this paper, the authors describe each step of the problem-solving algorithm employing efficient coding techniques focusing on improvement planning at Bit-error rsate (BER) vs signal-to-noise ratio (SNR) curves, in dense WSN, between CHs and the base station (BS) node. They use efficient coding techniques approach based on RNC and LDPC codes using the Time Division Multiple Access (TDMA) method. They generalize the notion of RNC-LDPC, study their properties and formulate the main steps of the proposed algorithm reconstruction method. The simulation results demonstrate that this approach is more effective in improving the BER using the proposed algorithm. Moreover, subsequently ameliorate the technical properties offered by this codification type.

Performance analysis of ZigBee network topologies for underground space monitoring and communication systems

The advancement in tunnelling and underground space technologies and the need for large scale monitoring and communication systems for safe and efficient operations has triggered the era of wireless sensor networks (WSNs). The progress of WSNs have been associated with the innovation of sensor nodes with the more significant features of smaller size, more cost-effectiveness, lower latency and powerful antenna coverage. The sensor nodes arrangement in dense industrial WSNs is one of the crucial issues for a better quality of service and a reliable message transmission through the network. In this study, we investigate various sensor node arrangements of ZigBee networks for underground space monitoring and communication systems. The performance of ZigBee topologies are analysed in 12, 20, 30, 40 and 50-node scenarios for stationary node deployment in underground environments. The metrics used for the performance evaluation include throughput, packet delivery ratio (PDR), end-to-end delay, energy consumption and packet delivery security. The results evaluation confirms the mesh topology is prioritised in WSNs design considering higher throughput, packet delivery ratio and network security, while the cluster-tree topology is preferred in case of lower end-to-end delay and lower energy consumption. The analyses show that the mesh topology creates a more reliable monitoring and communication network with an adequate quality of service in underground spaces and tunnels. Therefore, greater end-to-end delay and energy consumption could not be major concerns for the mesh topology in underground mine applications based on the acceptable data latency and using mine power.

Challenges in Wireless System Integration as Enablers for Indoor Context Aware Environments.

The advent of fully interactive environments within Smart Cities and Smart Regions requires the use of multiple wireless systems. In the case of user-device interaction, which finds multiple applications such as Ambient Assisted Living, Intelligent Transportation Systems or Smart Grids, among others, large amount of transceivers are employed in order to achieve anytime, anyplace and any device connectivity. The resulting combination of heterogeneous wireless network exhibits fundamental limitations derived from Coverage/Capacity relations, as a function of required Quality of Service parameters, required bit rate, energy restrictions and adaptive modulation and coding schemes. In this context, inherent transceiver density poses challenges in overall system operation, given by multiple node operation which increases overall interference levels. In this work, a deterministic based analysis applied to variable density wireless sensor network operation within complex indoor scenarios is presented, as a function of topological node distribution. The extensive analysis derives interference characterizations, both for conventional transceivers as well as wearables, which provide relevant information in terms of individual node configuration as well as complete network layout.

Low Cost Recursive Localization scheme for High Density Wireless Sensor Networks

While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.

Partitioning Graph Drawings and Triangulated Simple Polygons into Greedily Routable Regions

A greedily routable region (GRR) is a closed subset of [Formula: see text], in which any destination point can be reached from any starting point by always moving in the direction with maximum reduction of the distance to the destination in each point of the path. Recently, Tan and Kermarrec proposed a geographic routing protocol for dense wireless sensor networks based on decomposing the network area into a small number of interior-disjoint GRRs. They showed that minimum decomposition is NP-hard for polygonal regions with holes. We consider minimum GRR decomposition for plane straight-line drawings of graphs. Here, GRRs coincide with self-approaching drawings of trees, a drawing style which has become a popular research topic in graph drawing. We show that minimum decomposition is still NP-hard for graphs with cycles and even for trees, but can be solved optimally for trees in polynomial time, if we allow only certain types of GRR contacts. Additionally, we give a 2-approximation for simple polygons, if a given triangulation has to be respected.