Decentralized average consensus in wireless sensor networks with probabilistic network links

Average consensus (AC) is a popular method for distributed innetwork averaging that is closely related to diffusion processes. Recently, it has been suggested that AC designs inspired by physical advection processes feature improved performance. In this paper, we show how such advection-diffusion inspired AC designs can be obtained for realistic network topologies. The resulting AC algorithms act like a blender that mixes the measured data. Numerical simulations corroborate the performance improvements of our blending approach to the design of AC algorithms.

The average consensus in wireless sensor networks is achieved under assumptions of symmetric or balanced topology at every
\ntime instant. However, communication and/or node failures, as well as node mobility or changes in the environment make the topology vary in time, and instantaneous symmetry
\nof the links is not guaranteed unless an acknowledgment protocol or an equivalent approach is implemented. In this paper,
\nwe evaluate the convergence in the mean square sense of a well-known consensus algorithm assuming a random topology and asymmetric communication links. A closed form
\nexpression for the mean square error of the state is derived as well as the optimum choice of parameters to guarantee fastest
\nconvergence of the mean square error.

Light detection and ranging (LiDAR) has the advantage of simultaneous localization and mapping with high precision, making it one of the important sensors for intelligent robotics navigation, positioning, and perception. It is common knowledge that the random measurement error of global navigation satellite system (GNSS) observations is usually considered to be closely related to the elevation angle factor. However, in the LiDAR point cloud feature matching positioning model, the analysis of factors affecting the random measurement error of observations is unsophisticated, which limits the ability of LiDAR sensors to estimate pose parameters. Therefore, this work draws on the random measurement error analysis method of GNSS observations to study the impact of factors such as distance, angle, and feature accuracy on the random measurement error of LiDAR. The experimental results show that feature accuracy is the main factor affecting the measurement error in the LiDAR point cloud feature matching positioning model, compared with distance and angle factors, even under different sensor specifications, point cloud densities, prior maps, and urban road scenes. Furthermore, a simple random measurement error model based on the feature accuracy factor is used to verify the effect of parameter estimation, and the results show that the random error model can effectively reduce the error of pose parameter estimation, with an improvement effect of about 50%.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

In the geodetic data processing field, most methods for dealing with inequality constraints model are based on additive random error (ARE) models, and there have been few studies on mixed additive and multiplicative random error (MAAMRE) models with inequality constraints. To address this problem, a MAAMRE model with inequality constraints is first established based on the definition of inequality constraint equations, and then, a corresponding parameter estimation algorithm is proposed based on the idea of an exhaustive search method. In addition, considering a MAAMRE model for an ill-posed problem, an iterative regularization solution for an ill-posed MAAMRE model is first derived, and then, a specific parameter estimation algorithm for an ill-posed MAAMRE model with inequality constraints is further proposed by applying the exhaustive search approach. Finally, the feasibility and advantages of the proposed algorithms are verified by global positioning system (GPS) elevation fitting model and digital terrain model (DTM) examples.Graphical

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

Generally, routing techniques are essential for Wireless Sensor Networks (WSNs) to deliver data packets to their destinations (also known as sinks). For a practical application, sensors are deployed to monitor environmental changes and events. Most WSNs detect specific events in their specific environments. But, different WSNs may monitor different events with different sensors in the same area. For example, a smart-home WSN deploys thermal sensors/meters to measure indoor and outdoor temperatures, body-temperature sensors to detect thief intrusion, etc. Also, in the underlying house, there is a health WSN which utilizes physiological sensors to monitor patients’ health condition. In other words, several WSNs of different purposes co-exist in the same geographical area. Currently, each WSN’s data delivery is independent from others’. Basically, if all sensors in such a multi-WSNs environment can share their routing paths/nodes and relay event packets for other WSNs, the delivery efficiency can be enhanced since many more sensors can be found there for packet relay. Consequently, the transmission energy can be reduced since energy consumed for wireless transmission is proportional to d2 where d is the transmission distance between sender and receiver. Therefore, in this study, we propose an energy saving routing mechanism, named Energy-Efficient Cooperative Routing Scheme for Heterogeneous Wireless Sensor Networks (EERH for short), in which several WSNs deployed in the same geographical environment form a heterogeneous sensor network and sensors relay packets for its own WSN and also for other WSNs. Routing paths are dynamically established according to the transmission directions of event packets and the residual energy of the underlying sensors and their neighbors. In addition, the packets routed to the same direction by the same sensors are aggregated to save delivering energy. Moreover, the network parameters of the EERH, like propagation delay of an event packet and the transmission distance of a sensor, are adjustable so as to satisfy the practical environment needs. Simulation results show that the EERH efficiently extends the lifetime of a heterogeneous WSN.

Most wireless sensor networks are driven with a battery. Lifetime maximization is thus very important when designing such networks. Clustering a network is an effective topology control scheme to enhance energy efficiency and scalability of large-scale wireless sensor networks. However, when using a wireless sensor network after a disaster, if sensor nodes cannot join any clusters, the detection area of the wireless sensor network is narrowed. Maximizing lifetime and minimizing the number of sensor nodes, that cannot join clusters is very important. In this work, we propose a lightweight clustering scheme for wireless sensor networks used in disaster relief. Simulation results show that the proposed clustering scheme is efficient and effective for maximizing the lifetime of a network and the number of communicable sensor nodes compared with other traditional clustering schemes.KeywordsWireless sensor networksClusteringLifetimeDisaster relief

The current research work in wireless sensor networks has mostly focused on the resolving issues related to power consumption and computational resource constraints in the wireless sensor networks .To achieve the same various specific routing schemes ,MAC and cross layered protocols and techniques have been proposed and designed .But with recent advances the privacy issues related to the data collected and transmitted by the wireless sensor networks had taken the center stage .Privacy preservation in wireless sensor networks has become more challenging because of the wireless nature of communication in WSN as well as its self organizing architecture. The present paper provides a comparative review of various privacy preserving mechanisms proposed and implemented in wireless sensor networks with respect to the privacy notions of k- anonymity and L-diversity. Along with the discussion and analysis the present work is an effort for the pavement of a way towards the future research in the field of privacy preservation in WSN.

In this paper we propose a gossip algorithm for average consensus in clustered wireless sensor networks called superposition gossiping, where the nodes in each cluster exploit the natural superposition property of wireless multiple-access channels to significantly decrease local averaging times. More precisely, the considered network is organized into single-hop clusters and in each cluster average values are computed at a designated cluster head via the wireless channel and subsequently broadcasted to update the entire cluster. Since the clusters are activated randomly in a time division multiple-access fashion, we can apply well-established techniques for analyzing gossip algorithms to prove the convergence of the algorithm to the average consensus in the second moment and almost surely, provided that some connectivity condition between clusters is fulfilled. Finally, we follow a semidefinite programming approach to optimize wake up probabilities of cluster heads that further accelerates convergence.

Wireless Sensor Networks (WSNs) are a special class of wireless ad-hoc networks where their performance is affected by different factors. Congestion is of paramount importance in WSNs. It badly affects channel quality, loss rate, link utilization, throughput, network life time, traffic flow, the number of retransmissions, energy, and delay. In this paper, congestion control schemes are classified as classic or soft computing-based schemes. The soft computing-based congestion control schemes are classified as fuzzy logic-based, game theory-based, swarm intelligence-based, learning automata-based, and neural network-based congestion control schemes. Thereafter, a comprehensive review of different soft computing-based congestion control schemes in wireless sensor networks is presented. Furthermore, these schemes are compared using different performance metrics. Finally, specific directives are used to design and develop novel soft computing-based congestion control schemes in wireless sensor networks.  

Mobility within Wireless Sensor Networks (WSNs) has been widely considered for data collection tasks, where mobile robots physically collect the data from the sensors and return to the base station. Although this approach has proven to be useful in prolonging the lifetime of these networks, it cannot meet the requirements of real-time data collection tasks. For such tasks, we need to utilize mobile robots to create a connected path from the base station to the event, as well as use in-network forwarding through that path. This will provide a longer lifetime while addressing efficiency and scalability issues because mobile robots have a larger and renewable energy reserve, a longer transmission range, and capacity. One of the fundamental problems is how to coordinate robots to establish a connected path from the event location to the base station. We consider this fundamental problem with two objectives, namely minimizing distance traveled by the robots and minimizing hop count (the number of robots used on paths) under the constraints of satisfying the path and/or network connectivity. After mathematically formulating the underlying problems and discussing their NP-hardness, we propose various heuristic solutions. We then demonstrate the efficacy of our proposed solutions through extensive simulations.

One of the most important constraints in wireless sensor networks is the energy efficiency problem. To maximize the wireless sensor networks lifetime after the sensor nodes deployment, dynamic power management (DPM) should be carefully taken into account in wireless sensor networks. The goal of DPM is to reduce power dissipation by putting the sensor node into different states. In this paper, a new method of DPM based on wavelet neural networks is proposed to conserve energy. There are two salient aspects to this approach. First, the next event's time which is a non-stationary series is predicted as accurate as possible by wavelet neural networks. Nodes in deeper sleep states consume lower energy while asleep, but incur a longer delay and higher energy cost to awaken. Second, the nodes state in which lie wireless sensor network should lie is decided through the predictable time associated with the threshold time, residual power. The simulation results show that the energy consumption is significantly reduced and the whole lifetime of the wireless sensor networks is greatly prolonged through the proposed method.

The development of wireless sensor networks is currently very rapid in various sectors. One of the implementations of the wireless sensor network is for monitoring temperature and humidity. In monitoring a room or a large area, many nodes are installed at a certain point. With the number of senses by each node so that one data that can represent the area is needed. To get one data that can represent the data in that area, we need a consensus method. In this study, the average distributed consensus method was used to obtain data representation from an area's wireless sensor network. After testing and simulating the ideal range between WSN nodes is 30 meters where the average percentage of detection errors reaches 0%. And the maximum capability is 33 meters with an average percentage of error detection of 46%. If given a barrier between WSN nodes, the distance that can be reached becomes shorter, with an ideal distance of 16 meters with an average percentage of error detection of 0% and the farthest distance reaching 21 meters with a detection error of 50%. The results of the consensus method simulation show that the more slots are filled with consensus data about the results, the faster and easier convergence will be achieved, in this case the monitoring results represent the real situation.