Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

This paper focuses on enhancement of lifetime in an event driven wireless sensor network (WSN). The network lifetime is enhanced by optimal deployment of sensor nodes in a given region as premature shortening of life takes place leading to uneven consumption of energy. In the proposed scheme, the senor nodes are linearly deployed over the sub-regions and the inter-node distance between the sensor nodes depends on the statistics of events sensed by the sensor nodes in multi hop mode of communication. The performance is measured with respect to the residual energy at each sensor node and total number of days spent by sensor nodes. The lifetime of the sensor network is defined as the survival of some fraction of nodes. For an approximate Poisson distribution for occurrence of events in different sub-regions, the variation of energy consumption in sensor nodes is negligibly small. Further if some nodes are having finite residual energy even after failure of some nodes, then those nodes can continue their communication in single hop mode and hence the lifetime of the network is further extended.

Coverage is one of the most important factors in evaluating the service quality of a wireless sensor network (WSN), which depends on the deployment of sensor nodes. Unreasonable deployment of sensor nodes will seriously affect the performance and reduce quality of service for WSN. Generally, sensor nodes are randomly distributed in the target area. This will result in overlapping coverage and weak coverage of sensor nodes to areas. In addition, the target area is complex and diverse where the size and shape of the target area are affected by the different application scenarios. This increases the difficulty for achieving maximum coverage. This paper proposes an optimization method of sensor nodes deployment based on a improved differential evolution algorithm for coverage area maximization in WSN. The improved differential evolution method uses two better individuals joint forces to pull the current individual's evolutionary direction where these two better individuals are selected through a tournament selection strategy and their influence scale for current individual evolution is controlled by two different scaling factors, which improves the search ability. In simulation, the results show that the improved method can better optimize the deployment of sensor nodes and achieve higher coverage rate.

Multi robot systems are defined as a collection of two or more robots that are capable of working autonomously while coordinating with each other. Three challenges emerge while designing any multi robot system. The robots have to coordinate their path planning or trajectory planning in order to avoid collision during the course of navigation, while collaborating tasks with other robots to achieve a specific end goal for the system. The other challenge, which is the focus of this paper, is the security of the entire multi robot system. Since robots have to coordinate with each other, any one of them being malicious due to any kind of security threat, can lead to a chain reaction that may compromise the entire system. Such security threats can be fatal if not dealt with immediately. This paper proposes the use of a Hybridized Blockchain Model (HBM) to identify such security threats and take necessary actions in real time so that the system does not encounter any catastrophic failure. The proposed security architecture uses ROS (Robot operating system) to decentralize the information collected by robot clients and HBM to monitor the clients and take necessary real time actions.

In this talk we first present models for deployment and interactions of wireless sensor nodes and explore their properties (such as connectivity and wireless access). Then we focus on two methods for maintaining network connectivity. The first one uses energy balance (i.e., guaranteeing the same per node energy dissipation in the network towards avoiding its early disconnection due to energy depletion of bottleneck regions). The second method suggests an incremental deployment of (potentially heterogeneous) sensor nodes. Both methods incur significant gains and satisfactory trade-offs to several performance metrics.

Development of Internet-of-Things requires the deployment of self-powered wireless sensor nodes (WSN). Towards this end, photovoltaic cells are often used to harvest ambient light energy. On the other hand, it is desirable to reduce the size of the WSN circuits as much as possible. However, WSN miniaturization will lead to less area available for solar cells and consequently, insufficient energy harvested to power the WSN. In this work, we develop a PV-Tower solar cell concept that offers a large active surface area for light absorption, while occupying only a small footprint. We demonstrate silicon-based PV-Tower solar cells with footprint areas of 0.2 mm2, 0.4 mm2, and 0.6 mm2, which can generate ≈80 times more power than conventional solar cells over the same footprint. We have also fabricated a small footprint PV-Tower module prototype that comprises 10 PV-Tower solar cells connected in series, which exhibits an open circuit voltage of 5.1 V. At the maximum power point, the module can deliver a voltage of 3.6 V and a power of ≈66.7 mW, which translates to an output power per footprint of ≈78.5 mW cm−2. By optimizing the module design, we estimate that there is room to significantly reduce its footprint and increase the output power per footprint by ≈10 times to 785 mW cm−2. The results show that the PV-Tower solar cell can serve as a compact small footprint photovoltaic energy harvesting device that can address the physical size constraints posed by miniaturization of the WSN circuit.

Network lifetime play a vital role in setting up an energy efficient wireless sensor network. The lifetime of the network can be improved by efficient deployment and scheduling of the sensor nodes inside the network. In this paper, on the basis of mathematically calculated upper bound lifetime of the network, the sensor nodes are efficiently deployed by using Simulated Annealing and Particle Swarm Optimization with pre-specified sensing range of the sensor nodes and on the second fold the sensor nodes are efficiently scheduled by applying the Simulated Annealing and Dempster Shafer Theory. The overall objective of this paper is to find the optimized location and schedule for sensor nodes. The comparative study shows that the simulated annealing and particle swarm optimization algorithms performs better for sensors deployment. Simulated annealing and Dempster Shafer theory achieves the goal to provide the different schedules with high efficiency.

Path planning is a fundamental method in solving mazes or moving robots traversing through open fields with obstacles. Q-learning method is a model-independent reinforcement learning method, which can be utilized in path planning optimization for robots in a multi-robot collaboration system. However, the efficiency of the traditional Q-learning algorithm is relatively low because of the adopted random exploration policy. In this paper, a Boltzmann Policy based Q-learning algorithm is proposed and applied into the problem of path planning optimization of a Multi-robot system. The method is composed of two parts, which are Q-learning and Boltzmann policy. Q-learning is a grid-based algorithm that can solve the low-dimensional path planning problems. Boltzmann Policy adopts statistical probability and simulated annealing, so it can help to avoid trapping in local optimum and provide global optimal solution. Player/Stage is used to evaluate the performance, which shows that the proposed Q-learning algorithm based on the Boltzmann policy can remarkably improve the efficiency of the multi-robot System, reducing the number of explorations and converging the process.

Over the last few years, IoT devices have multiplied their number and functionality. These devices are located all over the world and batteries are their main source of energy. In this paper the usage of wireless LoRaWAN sensor node in smart Internet of Things applications was analyzed. The study focuses on the reduction of power consumption from wireless LoRaWAN sensor nodes. An analysis of the energy consumption of the various components of the architecture of the wireless sensor nodes is performed. An energy model of the energy consumed by the sensor nodes is presented and analyzed. The result of the analysis provides an accurate identification of the main energy consumers, the magnitude of their consumption and an in-depth understanding of the key mechanisms that need to be applied to improve energy efficiency in wireless sensor nodes. The emphasis is on the presented energy efficient protocol. Three action scenarios from the sensor nodes are presented, outlining the main guidelines and strategies for improving the efficiency of the wireless nodes. In addition, the research results reveal the magnitude of energy savings that can be achieved by implementing the improved duty cycle mechanisms in wireless sensor nodes.

In Wireless Sensor Networks (WSNs), the coverage means how well a sensor network will monitor the total area of the region. Node deployment is one of the important issues that need to be solved. The number of sensor nodes has direct impact on the cost of the Wireless Sensor Networks. So, keeping this in mind, the proper deployment of nodes can reduce the complexity of problems, energy consumption can be reduced and thus extends the lifetime of the Wireless Sensor Networks. In this paper, we are considering the tactical environment, so the deployment of sensor node becomes random. We are forming the tessellation of the deployment as Rhombus other than Triangle, Square and Hexagon that are taken ideally for deploying the sensor nodes and after applying the proposed method the life time of the given network increases.

We consider the problem of energy efficient random deployment of sensor network. Our goal is to find the sensor node density, or alternatively, the energy resource density at every point inside a given deployment region, which results in allocating the minimum total number of deployed sensors, or alternatively, the minimum total energy source subject to constraints on the quality of monitoring (QoM) and network lifetime. The QoM is defined as the average of spatial distortion in reconstructed signal at the base station and can be bounded for a random deployment of sensor nodes when sensors are points of a Poisson process in the deployment region. To solve the optimization problem, we first determine a node density which satisfies the QoM constraint. Next we present a continuous space model for random deployment with the associated routing scheme that can be used to provide the minimum total required energy consumption. Finally, we present a spatial distribution of the sensor nodes (or the energy resources) that can achieve this minimum total energy. Simulation result shows that the minimum total energy obtained is close to the actual energy required in a randomly deployed dense network.

WSN is deployed for the dissemination of various sensor nodes in a fixed topology to sense the environment with limited resources, and to communicate the sensed data with the base station through cluster head. WSN is one of the dynamic networks, which can perform several dynamic functions like change in cluster head, avoid redundant messages, resource reservation mechanism and resource cancellation mechanism. One of the major problems in the deployment of WSN is ‘terrain structure’. Due to irregular terrain structure, deployment of sensor nodes is random in nature and due to this random deployment; nodes are not properly organized in a distributed way. A Compressive Family-based Efficient Trust Routing Protocol is proposed by dividing the network into various clusters and then split up clusters into sub-clusters. Each sub-cluster is again separated into various families and each family is allocated with a family head. The performance of proposed approach is compared with similar protocols (GEED-M, EETRP, and FERP) developed for specific terrain structures like plateaus and military areas. The Compressive Family-based Efficient Trust Routing Protocol enhances the shelf life of the network by 69%, and reduces the energy consumption of the network by 30%.

A multi-Mobile Agent and optimal itinerary planning-based data aggregation in Wireless Sensor Networks

A wireless pressure sensor node design method and realization is put forward aiming at problems of dynamic pressure test in the hermetic cavity or cabin, such as high wiring difficulty and influence on air flow field. Dynamic gauge pressure signal is transmitted by a wireless sensor node in consideration of temperature affection factors. The sensor node is mainly composed of gauge and atmospheric pressure, temperature sensor, signal acquisition and modulation module, wireless RF module, data memory, lithium battery. On the basis of the above, the prototype is developed. The effectiveness and reliability of the wireless pressure sensor node are verified in means of sea trial test.

Now a days, it is essential, to improve the security including energy efficiency in various emerging application under WSN. To keep this in mind, a WDT node based sensor node master operations in wireless sensor network method is proposed. In this approach, network consists of random node deployment of sensor nodes and it is partitioned into levels. WDT is a watch dog timer based node acts as a station keeping node. In each level there is main selection and redundant selection of WDT node is considered. At a time one node is taken to monitor the other nodes in the level. By this all node health status is keep intact in different levels by the help of status word. Master operations includes, node information through status word, routing tree discovery for path used for data communication, efficient encryption technique in the path from source to sink via Key Distribution Centre(KDC). All this operations is carried out by WDT node and sensor nodes used in the path. The proposed approach also tells, how effectively energy level is maintained in the network. By implementing the WDT algorithm, routing tree and secured key based encryption methods the overall throughput, packet delivery ratio (PDR), End-to-End delay and energy may effectively manage.

Sensor deployment is one of the key topics addressed in Wireless Sensor Network (WSN). This paper proposes a new deployment technique of sensor nodes for WSN called as Quasi Random Deployment (QRD). The novel approach to deploy sensor nodes in QRD fashion is to improve the energy efficiency of the WSN in order to increase the network life time and coverage. The QRD produces highly uniform coordinates and it systematically fills the specified area. Along with Random Deployment (RD) pattern of wireless sensor node QRD is analysed in this study. The network is simulated using NS-2 simulator. The efficiency of this deployment is evaluated assuming three routing protocols used for the adhoc networks. They are AODV, DSDV, and DSR. The protocol assumed at transport layer is UDP. Application layer generates the CBR traffic. The performance of these three routing techniques is compared based on total energy consumption, coverage area. The simulation results show that the conventional routing protocols like DSR have a best performance for both RD and QRD of the sensor nodes when there is no mobility of the sensor nodes as compared to AODV and DSDV. Among AODV and DSDV, AODV performs better as compared to DSDV.