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The reliable distributed monitoring system provides the real-time video observation from the power cable tunnels when the preinstalled communication system is disabled in an emergency. The spherical robotic prototypes are designed as the sensor node based on the analysis of the robot dynamic model. A novel self-constructed mobile wireless sensor network (MWSN) is proposed to realize the communication and positioning functions through the fusion of the Ad-Hoc and ultra-wideband (UWB) approaches. The Ad-Hoc network consists of multiple robot nodes as sensor carriers and relay stations to transmit large amounts of data from the accident scene to the monitoring terminal. The UWB network consists of UWB tags and bases on each node and implements the leader-follower (LF) strategy to create the robot formation. In order to maximize the monitoring area and maintain the reliability of the network, the traditional LF strategy is optimized based on the N-X algorithm subjected to the constraints of node number, Ad-Hoc communication and UWB positioning range, and the power tunnel boundaries. A self-constructed MWSN consisting of five robotic nodes ( $N=5$ ) is capable of covering 40 m of power cable tunnel, when the value of $X$ is selected as 3. The network failure probability reduces from 73.5% ( $X=0$ ) to 0.2% ( $X=3$ ) when the malfunction probability of sensor node is 23.3% due to the accident condition in the tunnel. Finally, the simulation and experimental results show that the optimized LF algorithm increases 20.2% coverage of the network in the curve formation. The network transmits a monitoring video streaming with a $320\times240$ pixel resolution and a delay less than 150 ms. The real-time video observation from the accident scene is significant to formulate the emergency countermeasure and investigate the direct cause of the accident.

Due to energy constraints and limited lifetime, sensor nodes are usually deployed densely in the target fields. Moreover, some applications do not require 100% coverage all the time. In view of these two concepts we may turn off some of the sensor nodes exploiting the redundancy of deployed sensor nodes. On the other side, the underlying communication logical topology is an important issue regarding energy consumption by the network. To mitigate the constraints of sensor nodes researchers employ a variety of logical topologies among which chain oriented topology is one of the significant contenders. In this paper we combine all these concepts to present a novel node scheduling scheme by which we schedule the on-duty nodes in large sensor networks. The selected nodes then create multiple chains to communicate each other. Chain oriented topology saves more energy compare to other topologies like cluster based or flat topology. Moreover, choosing only a selected number of sensors, instead of choosing all of them saves more energy and lengthens lifetime of the network. The main criterion to select a sensor node we propose in this paper is the use of shared sensing region of a sensor node with its neighbours. Simulation results show that our protocol saves a large number of sensor nodes while maintaining significant amount of coverage.

This paper present a low-cost yet effective localization scheme for the wireless sensor networks. There are many studies in the literature of locating the sensors in the wireless sensor networks. Most of them require either installing extra hardware or having a certain amount of sensor nodes with known positions. The localization scheme we propose in this paper is range-free, i.e., not requiring extra hardware devices, and meanwhile it only needs two anchor nodes with known position. Firstly, we install the first anchor node at the lower left corner (Sink X) and the other anchor node at the lower right corner (Sink Y). Then we calculate the minimum hop counts for each unknown node to both Sink X and Sink Y. According to the minimum hop count pair to Sink X and Sink Y of each node, we can virtually divide the monitored region into zones. We then estimate the coordinate of each sensor depending on its located zone. Finally, we adjust the location estimation of each sensor according to its relative position in the zone. We simulate our proposed scheme and the well-known DV-Hop method. The simulation results show that our proposed scheme is superior to the DV-Hop method under both low density and high density sensor deployments.

With Multi-Objective Optimization (MOO) mechanisms, many practical scenarios are imitated in Wireless Sensor Networks (WSNs). In MOO numerous desirable conflicting or non-conflicting objectives contend with one another and the decision has to be done among multiple available solutions. Based on the type of situation, Programme, and issue to be solved, the MOO problem has varied solutions. The solution chosen is a tradeoff solution on several occasions. In WSN, it is possible to identify MOO issues and associated solutions based on network architecture, node deployment, MAC strategies, routing, data aggregation, node mobility, etc. In this context, the paper proposes mobility aware, competent; delay tolerant Energy Efficient Hybrid Routing Protocol (EEHRP). Optimizing several metrics to pick the best route from the source to the target node is the cornerstone of the EEHRP. Multi-Objective optimization from optimization theory is a NP-hard problem. EEHRP seeks to obtain a Pareto optimal solution for the section of best MOO-based route under sensor node. The simulation results demonstrate that, relative to state-of-the-art solutions, EEHRP is efficient in terms of energy, throughput, delay, control- and routing-overheads. Furthermore, the paper investigates statistical significance of the findings obtained across confidence intervals. To prove EEHRP’s competence, a confidential interval of 95% is inserted into the simulation results obtained to represent margin of error around the estimated points. The on-hand state-of-art solutions and the propensity of the research fraternity in relation to MOO are also analyzed in this paper.

Targeting a growing number of promising applications sink mobility has been measured as a good strategy to prolong the lifetime of mobile wireless sensor network MWSNs. Mobile sink helps indirectly achieving uniform energy—consumption and provides load balancing to enhance data collection from the sensor node. Generally routing in MWSN is more complicated as it operates in low power, battery constraint with limited resources. A recent development in mobile sink leads to researcher to design routing algorithms for MWSN but still many unanswered gaps like shortest path, node mobility prediction, route reconstruction, and maintaining network coverage under movement and multi-sink approach for data collection towards critical events from sensor nodes. This paper address taxonomy of various data collection approaches under the routing protocol schemes sensor field. Based on this we classify routing schemes as location-based and query-driven approaches. Finally, discussion summarizes the detection of some unanswered issues and also gives a path to researchers to design and enhancement to improve existing routing schemes for future direction.

Instead of using static nodes in wireless sensor network, implementation of hardware and mobile environment are challenges of mobile wireless sensor network. Mobile sensor nodes combined with wireless network is one of research area and mobility is one of key factors in designing mobile wireless sensor network. To analyze vehicle mobility, this work implements a mobile sensor node in WiFi network and it is equipped with ultrasonic sensor and ESP8266 NodeMCU. The sensor data can be monitored on ThingSpeak over the internet via wireless LAN and its measurement can be taken within 10 s delay. MikroTik (RB931-2nD) router is used as an access point in system architecture. Experiments are performed to study mobility effects on network throughput when mobile sensor node is moving with constant velocity of 10 cm/s. With connectivity in wireless LAN 802.11/n technology, signal strength and throughput are measured for vehicle mobility.

Wireless sensor network is a network of sensor nodes which are connected together wirelessly. Sensor node has the capability to sense, process and communicate the data to the distinct location which is known as base station or sink or destination sensor node. The purpose of sensor node is to sense the data and transmit to sink but the transmission of data is crucial which depends on how it is routed. Another factor which affects the sensor network, is energy. So These two factors are very crucial for WSN. Hence in the chapter we will cover the routing protocols used in wsn in detail. We will cover the types of sensor node, how can be routed the data using sensor node and how we can classify routing protocol so that we can have energy efficient network. We also discuss different IEEE standards related to WSN.

For the effective functioning of a Mobile Wireless Sensor Networks (MWSN), the connectivity maintenance of the sensor nodes is of significant concern. Otherwise, it may result in an independent node or nodes wholly get detached from the network. Though such detached sensor nodes are functioning correctly and have good energy backup, its service cannot be utilized for the purpose it is intended for as it is isolated from the core network. These sensor nodes are sophisticated tiny devices and costlier depending on the application; therefore, proper care should be taken to keep them connected to the network. Hence, a firefly based algorithm, a Swarm Intelligence technique, referred to as Firefly Algorithm for Connectivity in Mobile WSN (FACM) has been proposed in this article in order to establish proper connectivity among the sensor nodes in MWSN. FACM is based on the insect fireflies, which have a unique feature of producing light, a result of chemical reaction, at different intervals to escape from the predators and most of the time to attract prey. The inevitable feature of insect firefly, attracting the prey, is exploited in the proposed FACM where a brighter sensor node (in terms of energy and distance) will attract the less bright neighboring sensor nodes. Thus, the less bright sensor node can depend on the brighter sensor node for the data transfer, thereby saving its energy. A fitness function has been designed based on the combination of two parameters energy and the distance, which decides the brightness of the sensor node. The effectiveness of the proposed FACM has been theoretically analyzed and verified by simulation through MATLAB. The results obtained are compared with classical FA and are found to be inspiring.

With the emergence of Internet of Things (IoT) technology, a huge number of sensor based applications are going to be deployed. Thus, due to their valuable contribution, these sensors have to operate consistently and fulfill their task efficiently. To this end, the communications they involve must be optimal to comply with the expected requirements of IoT users. Bio-inspired computing paradigm has exhibited its capability to offer the best outcomes in information communications over wireless sensor network and vehicular ad hoc network environments. Providing mobility to sensor nodes in these networks enables to substantially improving ability of data sensing through energy saving, and data delivery through node proximity collecting. Indeed, this improvement is still also of great importance in IoT sensor environments. This work aims at increasing the knowledge of the readers by providing sufficient and comprehensive backgrounds in biologically inspired algorithms used for satisfactorily solving challenges posed by different sensors' mobility schemes in the context of IoT applications. Therefore, we give a global overview of static and mobile sensor node strategies with details related to Mobile Wireless Sensor Network (MWSN) enabling technologies. A set of routing protocols based on nodes' mobility will be succinctly examined according to classical domain as well as to bio-inspired one, in order to bring out what should be suitable for IoT applications depending on sensor nodes as building blocks.

Purpose This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique. Design/methodology/approach In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs. Findings Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively. Research limitations/implications In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only. Practical implications To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m2 network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s. Practical implications This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption. Originality/value PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.

In wireless sensor networks, sensor deployment is an important issue in which sensors are deployed in a specific monitoring region using low-cost hardware but achieving high coverage quality. In recent years, several mechanisms have been developed for the efficient robotic deployment of sensors. Their performance levels are highly dependent on unknown obstacles that can give rise to the dead-end problem. A key challenge when developing a robot deployment mechanism is to overcome the dead-end problem and deliver full coverage with a minimal number of sensors. This paper proposes an impasse-aware robot deployment (IAD) algorithm. The proposed IAD mainly consists of basic deployment rules and dead-end handling rules. The basic deployment rules are intended to achieve full coverage with a minimal number of sensors; the proposed dead-end handling rules can efficiently resolve the dead-end problem. Extensive experimental studies have demonstrated that our proposed IAD achieves superior performance to that of existing robot deployment mechanisms with respect to coverage ratio, energy efficiency, deployment path length, and required stack space.

Wireless sensor networks have extensively been utilized over the years for ambient data collection from diverse structural deployments including mesh, ad hoc , and hierarchical layouts. Several other applications of sensor networks may involve placing the nodes in a linear topology, constituting a special class of networks called linear sensor networks. In a densely deployed linear network case, issues related to optimal resource allocation and networking may persist because the standard sensor network protocols attempt to manage the network as a mesh or ad hoc infrastructure. Issues like recovering from holes where a node cannot reach another node in either side or policies to establish a route for data dissemination need to be intelligently solved for linear sensor networks. To solve such issues, we propose a linear sensor network deployment application for oil and gas pipeline using a custom sensor board accompanied with algorithms to solve network creation, leak interrupt detection, and routing of high-priority messages with reliability while keeping network alive at all times. The proposed system provides all the features of leakage detection, localization, parameter sensing, and actuation, while operating at low energy, high data reliability, and low latencies, while comparative results prove the efficacy of the system.

A wireless oceanographic system is an underwater wireless-networked sensing system for oceanographic data collection, remote monitoring, and control. It represents a cutting-edge technology that could eliminate the need of long and expensive subsea cables while featuring real-time acquisition, flexible, and convenient deployment. Mainly powered by batteries, the wireless oceanographic system calls for power efficient deployment plans to extend the system lifetime. In this work, we first propose an architecture of wireless oceanographic system and then introduce a framework for the deployment planning. Specifically, we investigate the power consumption profile of the sensor nodes in the system. We employ a battery model for more accurate power estimation. Further, we analyzed the system lifetime with the consideration of the sensor node interactions. As a case study, the framework is applied to the Ocean-TUNE Long Island Sound (LIS) testbed, a real wireless oceanographic system with practical system configurations, and real data.

Summarization: An extensive utilization of wireless sensor networks has evolved during the last years for monitoring various environmental and artificial processes. When operating in remote locations, the nodes of wireless sensor networks are typically power supplied by an energy production and management system, comprising low-power renewable energy sources, a power electronic converter, and a battery-based energy storage unit. In this paper, a methodology is proposed for optimally designing the energy production and processing system of a wireless sensor network node simultaneously at both the renewable power-supply system level and the power converter circuit level, through a unified design process. The impact of the objective function type on the power-supply design is also investigated in this paper. Design optimization and experimental results are presented, which demonstrate that the optimized power-supply structures derived by applying the proposed optimization technique exhibit lower cost of generated energy compared to partially optimized or totally nonoptimized structures and by that reduce the cost of the overall wireless sensor network node. Presented on: IEEE Transactions on Industrial Informatics

This paper investigates the problem of energy consumption in wireless sensor networks. Wireless sensor nodes deployed in harsh environment where the conditions change drastically suffer from sudden changes in link quality and node status. The end-to-end delay of each sensor node varies due to the variation of link quality and node status. On the other hand, the sensor nodes are supplied with limited energy and it is a great concern to extend the network lifetime. To cope with those problems, this paper proposes a novel and simple routing metric, predicted remaining deliveries (PRD), combining parameters, including the residual energy, link quality, end-to-end delay, and distance together to achieve better network performance. PRD assigns weights to individual links as well as end-to-end delay, so as to reflect the node status in the long run of the network. Large-scale simulation results demonstrate that PRD performs better than the widely used ETX metric as well as other two metrics devised recently in terms of energy consumption and end-to-end delay, while guaranteeing packet delivery ratio.

This paper introduces a new class of microwave sensor architecture using direct conversion principle to eliminate data processing and provide low-power smart sensor nodes. Using a six-port circuit as the modulator, the sensing data are up-converted directly to a microwave frequency and sent by an antenna. At the receiver, a six-port circuit is used as a demodulator to down-convert the received signal and extract the sensing data. To predict the communication channel of the sensor network and provide a robust connection, a pilot sequence is sent along with the sensor data which is used in the calibration procedure at the receiver for accurate extraction of sensing data and making sensors smarter. Any kind of microwave sensors based on the variation of impedance can be used in the proposed structure. In this paper, the capacitive loading is used as a material sensor. By variation of material in the sensing area, the impedance of the load and reflection coefficient ( $\Gamma$ ) are changed. Then, the amplitude and phase of $\Gamma$ are transmitted via the six-port circuit, received in the network base station, and analyzed to find the material changes. As an example, a system is fabricated and evaluated at 2.45 GHz.

This paper deals with the problem of environmental monitoring by developing an event-triggered finite-time control scheme for mobile sensor networks. The proposed control scheme can be executed by each sensor node independently and consists of two parts: one part is a finite-time consensus algorithm while the other part is an event-triggered rule. The consensus algorithm is employed to enable the positions and velocities of sensor nodes to quickly track the position and velocity of a virtual leader in finite time. The event-triggered rule is used to reduce the updating frequency of controllers in order to save the computational resources of sensor nodes. Some stability conditions are derived for mobile sensor networks with the proposed control scheme under both a fixed communication topology and a switching communication topology. Finally, simulation results illustrate the effectiveness of the proposed control scheme for the problem of environmental monitoring.

In a wireless sensor network, wireless Energy transfer is a demanding technology for the energy difficulties in recent times. The foremost disadvantage of presentation is limited duration because WSN contains only restricted battery energy at a node. Therefore, we anticipated cluster-related wireless energy transfer in this document. The foremost intention of the method is to augment the duration of the sensor network through charging by the help of this wireless power transfer technology. So that, mobile charging vehicle (MCV) is established to move within the network and charge the sensor node battery wireless. The sensor nodes in the network are collected as a cluster for energy efficiency. Here, the cluster head is chosen for each one cluster in the network which is based on the rank metric value. Suppose, if one node in the network is reducing its energy, then the CH will send charge request and route ID to the MCV. Afterward, the MCV recognize the node by means of the exacting route and establish to charge the node. The reproduction consequences illustrate that the network lifetime of our anticipated method is enhanced than obtainable method.

A challenging research topic in wireless sensor network is prolonging the network lifetime by optimizing the network energy expenditure. The paper explores an enhanced energy optimization routing protocol which uses double cluster heads in a clustering wireless sensor network. The double cluster heads which are determined by energy welfare function are referred to as main cluster head and assistant one, respectively. To balance nodes energy, a formula for the energy overhead is presented to make the sensor node join a temporary cluster with low energy consuming during cluster set-up stage. Then, during the routing selection, relay nodes communicate sensory information to the sink by using combining single-hop and multi-hop technology. The selection of relay nodes depends on the weight values involving information of residual energy, the number of nodes in the cluster, selected times as relay nodes, and a distance between two nodes. The improved protocol is evaluated via simulation experiments and compared with some existing protocols. The simulation results demonstrate that the proposed protocol outperforms the relevant protocols in energy utilization efficiency. Especially, the network lifetime is prolonged due to decreasing energy consuming of the cluster head.

Energy conservation in wireless sensor networks (WSNs) is a fundamental issue. For certain surveillance applications in WSN, coverage lifetime is an important issue and this is related to energy consumption significantly. In order to handle these two interlinked aspects in WSN, a new scheme named Weight based Coverage Enhancing Protocol (WCEP) has been introduced. The WCEP aims to obtain longer full coverage and better network life time. The WCEP is based on assigning different weight values to certain governing parameters which are residual energy, overlapping degree, node density and degree of sensor node. These governing parameters affect the energy and coverage aspects predominantly. Further, these four different parameters are prime elements in cluster formation process and node scheduling mechanisms. The weight values help in selection of an optimal group of Cluster Heads and Cluster Members, which result in enhancement of complete coverage lifetime. The simulation results indicate that WCEP performs better in terms of energy consumption also. The enhancement of value 24% in full coverage lifetime has been obtained as compared to established existing techniques.

Although the field application of Wireless Sensor Networks (WSNs) is widely varied, their use in practice is nominal due to the fact that existing sensor nodes do not meet necessary requirements, which are sensor integration with nodes and sending of sensed information to remote stations. In fact, sensor nodes face a number of challenges, such as scripts that cannot be fully changed, network routing algorithms not being written, and not receiving data from the most sensors. Therefore, a new sensor node has been developed (called WiSeN) which aims to be usable in all WSN applications. WiSeN has been developed for unlimited numbers and multiple types of sensors that can be connected, and it was designed to create a new and user-friendly alternative to sensors currently being used. (C) 2017 Elsevier Ltd. All rights reserved.

Most of researches on wireless sensor network (WSN) are focused on how to reduce energy consumption to increase the network lifetime. There are few researches solving the problem of energy optimization ensuring delay constraint. Whereas, the delay is important factor for applications that require delay-sensitive data. Several works have been published to balance the energy consumption and delay. They achieved many different results but each proposal has certain limitations. Some proposals have high computational and messaging complexity. Some others have not found the optimal solution. In this study, we investigate in finding solution to improve the energy efficiency of sensor nodes satisfying end-to-end delay to transmit data from sensor nodes to sink in multi-hop WSNs. Based on Lagrange relaxation method, we propose an aggregate cost function between energy consumption and delay as well as an efficient method to find the optimal multiplier for that objective function. We provide two algorithms to find paths with least energy consumption while maintaining end-to-end delay requirement from any sensor node to sink. Besides analyzing the complexity and convergence of the algorithm, the simulation results also show that the proposed algorithm achieved good balance between energy consumption and delay compared with the previous proposals.

A typical pervasive monitoring system like a smart building depends on an infrastructure composed of hundreds of heterogeneous wireless sensor devices. Managing the energy consumption of these devices poses a challenging problem that affects the overall efficiency and usability. Existing approaches for sensor energy consumption typically assume a single monitoring application to consume sensor data and a static configuration for sensor devices. In this paper, we focus on a multi-application context with dynamic requirements and multi-modal sensor devices. We present 3SoSM, an approach to optimize interactions between application requirements and wireless sensor environment in real-time. It relies on an energy-aware dynamic configuration of sensor devices to lower energy consumption while fulfilling application requirements. To bind together sensor configuration and dynamic management of data streams, we design a sustainable multi-application monitoring system architecture for pervasive environments that collects application requirements for sensor data streams and optimizes them into sensor configurations. To demonstrate the effectiveness of our approach, a set of experiments are designed in the context of smart buildings. We comparatively evaluate our approach to show how dynamic sensor configuration for multiple monitoring applications indeed outperforms the mainstream duty-cycling method. (C) 2017 Elsevier B.V. All rights reserved.

Wireless sensor networks (WSNs) consist of large numbers of micro-sensor nodes that have both communication and computing capabilities. The size of the coverage area of each sensor node determines the overall performance of the WSN. In this paper, a novel sensor deployment scheme based on the social spider optimization (SSO) algorithm is proposed to enable increased coverage for WSNs. In the SSO algorithm, the individual simulated social spiders’ habits and their labor and cooperation efforts are divided according to gender differences. Simulations experiment are used to demonstrate the effectiveness of the proposed method.

Wireless sensor networks (WSNs) are finding application in numerous fields. For many of these applications, some or all of the sensor nodes are mobile. Mobility management in WSNs is a challenging and has been the subject of intensive research. In this paper, we propose an IP-based mobility management protocol named Fast handover Proxy Mobile IPv6 for Sensor networks (FPMIPv6-S). FPMIPv6-S provides network-based mobility support for 6LoWPAN WSN when a sensor node moves between two Personal Area Networks. It is an improved version of the PMIPv6 protocol proposed for mobility management in IPv6 networks. FPMIPv6-S messages are carried by RPL protocol. Analytical evaluation of the proposed protocol shows significantly improved performance with respect to network mobility cost (signalling traffic) when compared to PMIPv6 and RPL alone. We also integrated the FPMIPv6-S protocol in the NS-3 simulator and performed a thorough performance comparison of FPMIPv6-S and RPL.

Unmanned aerial vehicle (UAV)-assisted microwave wireless power transfer (WPT) enables the deployment of radio frequency (RF)-power-driven, battery-less sensor nodes in rural areas such as farm fie...

An information fusion framework is proposed for robot-sensor network cooperative localization.A parallax-based tracking method is designed for sensor nodes to localize the robot.Information from both the robot's SLAM and nodes' target tracking is integrated closely.Cooperative localization can achieve a better performance than the other methods. Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be significantly enhanced using the proposed method.

It is well understood that data-acquisition by distributed sensors and subsequent transmission of all the acquired data to the cloud will produce a “data deluge” in next-generation wireless networks leading to immense network congestion, and data back-logs on the server which will prevent real-time processing and control. This motivates in situ data analytics in energy-constrained wireless sensor nodes that can perform context-aware acquisition and processing of data; and transmit data only when required. This paper presents a camera-based wireless sensor node with a self-optimizing end-to-end computation and communication design, targeted for surveillance applications. We demonstrate support for multiple feature-extraction and classification algorithms, tunable processing depth and power amplifier gain. Depending on the amount of information content, accuracy targets and condition of the wireless channel, the system choses the minimum-energy operating-point by dynamically optimizing the amount of processing done on the sensor itself. We demonstrate a complete system with ADI ADSP-BF707 image processor, OV7670 camera sensor, and USRP B200 software defined radio; and achieve $4.3\times $ reduction in energy consumption compared with a baseline design.

Increasing the lifetime of the network and utilizing the resources to its maximum limit is the major issue in Wireless Sensor Network (WSN). The wireless sensor nodes in sensor network are powered using rechargeable batteries. However, providing energy to nodes in the remote environment is a major issue in WSN. Hence WSN needs a new energy efficient algorithm to enhance the network lifetime. In a sensor node, the transceiving module consumes more energy when compared to other modules. In this paper, a Battery Recovery based Lifetime Enhancement (BRLE) algorithm is discussed, which considers battery voltage curve for scheduling the transceiving module of the sensor nodes. The Markov model helps in determining the state of the sensor node as CH and CM based on battery recovery process. By scheduling the transceiving module based on the battery terminal voltage, recovery factor and distance between the nodes, the lifetime of the network is enhanced. Experimental results show that the algorithm outperforms the others by 1.38 times increased lifetime and 1.574 times increased throughput. The BRLE decreases the HOT SPOT and energy hole problem, avoiding loss in connectivity with the sink.

Sensor nodes are small and low-cost portable devices that are connected wirelessly to form a wireless sensor network. Sensor nodes are typically powered by a chemical battery source that has a load-dependent finite lifetime. Most applications require wireless sensor networks to operate reliably for an extended period of time beginning with their deployment. To ensure both the longevity of a wireless sensor network via power management techniques and development of a self-powered sensor node, it is important to understand a sensor node’s operational energy consumption. It is well understood that wireless sensor network lifetime has a strong functional dependence on sensor node lifetime. This paper presents the development of a stochastic model to capture the expected energy consumption of a schedule-driven sensor node per cycle of operation by utilizing renewal theory, random stopping criteria, and Wald’s inequality. The model captures the expected energy consumed due to generic operation of a schedule-driven sensor node when an external event occurs (i.e., node level activities) and energy consumed due to carrier sense multiple access/collision avoidance medium access control protocol (i.e., retransmission attempts, channel listening, and other network level activities). The stochastic energy model is used to compute the expected lifetime and cycle lifetime of the sensor node. This paper also computes the deterministic energy bounds corresponding to the minimum and maximum energy consumption of a sensor node per cycle of operation. Analytical and numerical model validations are performed and presented.

In many applications of Wireless Sensor Networks (WSNs), the network has a few mobile sinks in addition to a large number of static sensor nodes that gather information from their surrounding environment. The static sensor nodes in such applications use a location service to find the location of a mobile sink. Most of the proposed location services are useful in mobile ad hoc networks and are not efficient for WSNs. Furthermore, new protocols specifically designed for WSNs are not load balanced. Some sensor nodes relay more packets and deplete their energy quickly, hence reducing the network lifetime dramatically. In this paper, we propose a location service called Load Balanced Location Service (LBLS) for WSNs containing a few mobile sinks. LBLS selects four nodes in the network outline and only uses the location of these nodes. LBLS do not deplete the energy of specific nodes comparing to other mechanisms that forward location information messages toward selected nodes. We formally prove that in LBLS every sensor node can always locate any mobile node in the network. By using exhaustive simulation, we show that LBLS is a load balanced location service. Simulation results demonstrate the superiority of LBLS regarding the load balancing factor compared to most available related location services.

Wireless sensor networks have become increasingly popular due to their wide range of application. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. Minimizing the energy consumption of a wireless sensor network application is crucial for effective realization of the intended application in terms of cost, lifetime, and functionality. However, the minimizing task is hardly possible as no overall energy cost function is available for optimization. The need for energy-efficient infrastructures for sensor networks is becoming increasingly important. Wireless sensor networks are networks consisting of many sensor nodes that communicate over a wireless media. A sensor node is equipped with a sensor module, a processor, a radio module and a battery. Since the battery limits the lifetime of the sensor nodes it also limits the lifetime of the sensor network, thus energy efficiency is a major issue for sensor networks. An important goal in many sensor networks is to monitor an area as long time as possible. Hence, it is important to distribute energy consumption evenly across the network. When the energy consumption is evenly distributed, the major part of the sensor nodes will stay alive approximately equally long time.

Wireless sensor network (WSN) is comprised of tiny, cheap and power-efficient sensor nodes which effectively transmit data to the base station. The main challenge of WSN is the distance, energy and time delay. The power resource of the sensor node is a non-rechargeable battery. Here the greater the distance between the nodes, higher the energy consumption. For having the effective transmission of data with less energy, the cluster-head approach is used. It is well known that the time delay is directly proportional to the distance between the nodes and the base station. The cluster head is selected in such a way that it is spatially closer enough to the base station as well as the sensor nodes. So, the time delay can be substantially reduced. This, in turn, the transmission speed of the data packets can be increased. Firefly algorithm is developed for maximizing the energy efficiency of network and lifetime of nodes by selecting the cluster head optimally. In this paper firefly with cyclic randomization is proposed for selecting the best cluster head. The network performance is increased in this method when compared to the other conventional algorithms.

As the energy efficiency remains a key issue in wireless sensor networks, duty-cycled mechanisms acquired much interest due to their ability to reduce energy consumption by allowing sensor nodes to switch to the sleeping state whenever possible. The challenging task is to authorize a sensor node to adopt a duty-cycle mode without inflicting any negative impact on the performance of the network. A context-aware paradigm allows sensors to adapt their functional behavior according to the context in order to enhance network performances. In this context, the authors propose an enhanced version the Efficient Context-Aware Multi-hop Broadcasting (E-ECAB) protocol, which combines the advantages of context awareness by considering a multi criteria and duty-cycle technique in order to optimize resources usage and satisfy the application requirements. Simulation results show that E-ECAB achieves a significant improvement in term of throughput and end-to-end delay without sacrificing energy efficiency.

Recently, the concept of barrier-coverage of wireless sensor network has been introduced for various civilian and military defense applications. This paper studies the problem of how to organize hybrid sensor network, which consists of a number of energy-scarce ground sensors with homogenous initial battery level and energy-plentiful mobile sensors, to maximum the lifetime of barrier-coverage. Two key observations are (a) as the lifetime of each mobile sensor is much longer than that of the static ground sensors, each mobile sensor is capable of contributing multiple sensor barrier formations, and (b) no mobile sensor node can join two hybrid barriers which will be successively used to continuously protect the area of interest due to the moving delay. Based on these, we introduce a new maximum lifetime barrier-coverage problem in hybrid sensor network. We first propose a simple heuristic algorithm by combining existing ideas along with our own. Then, we design another efficient algorithm for the problem and prove that the lifetime of hybrid barrier constructed by this algorithm is at least three times greater than the existing one on average. Our simulation result shows that the second algorithm outperforms the first algorithm at least 33 percent and up to 100 percent.

Recent research works suggest cluster head reselection as a mechanism for network lifetime improvement. Cluster head reselection is either energy or time dependent. However, frequent cluster head reselection increases time overhead and energy consumption leading to reduction in network lifetime. Hence, an attempt is made to minimise the cluster head reselection frequency. The cluster head reselection is carried out whenever the energy of current cluster head falls below the threshold and this threshold corresponds to the minimum energy required for a node to survive. It is this selection of the threshold value that favours reduction in cluster head reselection frequency. Further, the cluster head reselection is based on energy drainage parameters such as the rate of recurrent communication of sensor node in addition to the residual energy of the node and distance of the node from the base station. To demonstrate the effect of rate of recurrent communication of sensor node on cluster head reselection, experiments are conducted both through simulation and hardware. Further, to study the effectiveness of the proposal, comparisons are made with other algorithms such as low energy adaptive clustering hierarchy (LEACH)-C, adaptive decentralised re-clustering protocol and T-LEACH. Reduced cluster head reselection frequency exhibits low energy consumption and time overhead with enhanced network lifetime.

This paper presents the design of a wireless sensor network architecture suitable for monitoring vibration on an aircraft. The main objective is to improve the flight safety by detecting vibration intensity among all the components of the airplane structure and communicating this information to the cockpit computer to react before any component can reach resonance. Therefore, the study of sensor nodes deployment for specific types of airplanes and the definition of appropriate network topology are conducted. Furthermore, to enhance the responsiveness of the structural health monitoring system, a medium access protocol based on game theory was developed. The main idea is to adjust the wake time period of a sensor node according to the sensed vibration level. This parameter will be used to configure the game-based medium access protocol. The performance evaluation work shows that the proposed vibration and game-based communication scheme enhances the transmission efficiency as it reduces the packet loss and the average transmission delay.

This paper focuses on the theoretical modeling of sensor cloud, which is one of the first attempts in this direction. We endeavor to theoretically characterize virtualization, which is a fundamental mechanism for operations within the sensor-cloud architecture. Existing related research works on sensor cloud have primarily focused on the ideology and the challenges that wireless sensor network (WSN)-based applications typically encounter. However, none of the works has addressed theoretical characterization and analysis, which can be used for building models for solving different problems to be encountered in using sensor cloud. We present a mathematical formulation of sensor cloud, which is very important for studying the behavior of WSN-based applications in the sensor-cloud platform. We also suggested a paradigm shift of technology from traditional WSNs to sensor-cloud architecture. A detailed analysis is made based on the performance metrics, i.e., energy consumption, fault tolerance, and lifetime of a sensor node. A thorough evaluation of the cost effectiveness of sensor cloud is also done by examining the cash inflow and outflow characteristics from the perspective of every actor of the sensor cloud. Analytical results show that the sensor-cloud architecture outperforms a traditional WSN, by increasing the sensor lifetime by 3.25% and decreasing the energy consumption by 36.68%. We also observe that the technology shift to sensor cloud reduces the expenditure of an end user by 14.72%, on average.

The wireless sensor network (WSN) is an attractive technology, which combines embedded systems and communication networks making them more efficient and effective. Currently, WSNs have been developed for various monitoring applications. In this research, a wireless mesh network for a pipeline monitoring system was designed and developed. Sensor nodes were placed at each branch in the pipe system. Some router fails were simulated and the response of each node in the network was evaluated. Three different scenarios were examined to test the data transmission performance. The results proved that the wireless mesh network was reliable and robust. The system is able to perform link reconfiguration, automatic routing and safe data transmission from the beginning node to the end node.

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.

A dynamic energy-efficient network model that can reduce the energy dissipation of sensor nodes while improving on WSN lifetime is proposed. The Harmony Search Optimization Algorithm is the core that the model is built on it.The proposed model employs two approaches named a mobile base station and a cluster-based network technique to reduce the communicating distances between sensor nodes and their base station.The cluster-based network technique seeks to build a dynamic network infrastructure where the optimal number of clusters and their members is predicted.The mobile base station algorithm seeks to find the near optimal location (i.e. center) between the cluster heads that represent each cluster and the base station in a manner that can reduce the communicating distances and to be closer to all of them.The proposed dynamic network model runs into rounds including the sensing and sending stages until the last node dies. In wireless sensor networks (WSNs), the location of the base station (BS) relative to sensor nodes is an important consideration in conserving network lifetime. High energy consumption mainly occurs during data communication between sensor nodes and the BS, in both single and multi-hop infrastructures. A WSN design with dynamic BS relocation is therefore desirable as this may prolong the network operational lifetime. However, positioning the BS next to each sensor node may cause data gathering latency. The reorganization of sensor nodes into clusters and the choice of a delegate node from each cluster, known as a cluster head (CH), as a communicator between each cluster and the moving BS appears to avoid this latency problem and enhance the energy utilization in WSNs. In this paper, we propose an energy-efficient network model that dynamically relocates a mobile BS within a cluster-based network infrastructure using a harmony search algorithm. First, this model allocates sensor nodes into an optimal number of clusters in which each sensor node belongs to the most appropriate cluster. Following this, the optimal CHs are chosen from the other clusters sensors in order to evenly distribute the role of the CHs among the sensors. This infrastructure changes dynamically based on the number of alive nodes, so that load balancing is achieved among sensor nodes. Subsequently, the optimal location of the moving BS is determined between the CHs and the BS, in order to reduce the distances for communication. Finally, sensing and data transmission takes place from each sensor node to their respective CH, and CHs in turn aggregate and send this sensed data to the BS. Simulation results show very high levels of improvements in network lifetime, data delivery and energy consumption compared to static and random mobile BS network models.

In this letter, wireless powered sensor networks with randomly deployed power beacons (PBs) and distributed antenna elements (DAEs) are investigated. The transmission duration is divided into two phases: 1) wireless power transfer (WPT) phase and 2) information transmission (IT) phase. In the WPT phase, the sensor nodes harvest energy from the radio frequency signals radiated by the PBs. Then, the sensor nodes use their harvested energy to transmit the measured and acquired data to the central processing server via the randomly deployed DAEs in the IT phase. After modeling the locations of PBs and DAEs as independent spatial point processes, in the case that the path-loss exponent of WPT equals to 4, we derive the outage performance of a typical sensor node and show that there exist outage error floors when individually increasing PB transmit power, density of PBs, or density of DAEs. Finally, simulation results verify our analysis and show that for a target outage probability the density of PBs should be far larger than that of DAEs.

Unlike traditional homogeneous single-task wireless sensor networks (WSNs), heterogeneous and multitask WSNs allow the cooperation among multiple heterogeneous devices dedicated to solving different signal processing tasks. Despite their heterogenous nature and the fact that each device may solve a different task, the devices could still benefit from a collaboration between them to achieve a superior performance. However, the design of such heterogeneous WSNs is very challenging and requires the design of scalable algorithms that maximize the performance of the devices without transmitting their raw sensor signals in an uncontrolled fashion. Towards this goal, novel techniques are needed both on the signal processing level and on the network-communication level. In this paper, we give an overview of applications in the field of heterogeneous and multitask WSNs with special focus on the signal processing aspects. Moreover, we provide a general overview of the existing algorithms for distributed node-specific estimation. Finally, we discuss the main challenges that have to be tackled for the design of heterogeneous multitask WSNs.

Objective: This paper represents an energy efficient algorithm for localization of nodes in wireless sensor network by the help of a small number of anchor nodes (mobile). It calculates the node distance and node location. Method/Statistical Analysis: A small group of mobile dynamic anchor node is being associated with a powerful GPS receiver, Radio frequency and ultrasonic device. Location information is broadcasted periodically by the mobile anchor node and by trilateration/ multilateration technique, the static node location is calculated. To calculate the approximate position of a sensor node it takes three rounds. Findings: The technique is evaluated with the matlab and result compared with chord selection approach. The simulation result shows that the proposed localization method performs better and efficient than the other localization algorithms. Application/Improvement: The proposed algorithm adopts distance measurement as TDOA technique and static sensor node can localize when it comes into the reception range. The method is applicable location computation based routing, sensing nodes, node signal processing, and for node localization.

Cluster formation and cluster head selection are important problems in Wireless Sensor Network (WSN) applications and can drastically affect the network's communication energy dissipation. However, selecting the cluster head is not easy in different environments which may have different characteristics. In order to deal with this problem, in this paper, we implement an integrated fuzzy-based system for controlling sensor speed in WSNs. Different from our previous work, we consider 4 input linguistic parameters: Remaining Power of Sensor (RPS), Degree of Number of Neighbor Nodes (D3N), Distance from Cluster Centroid (DCC) and Sensor Speed (SS) for selection of the cluster-head and the control of sensor speed. By controlling the sensor speed, we are able to predict whether the node will leave or stay in the cluster. We evaluate the proposed system by simulations and show that the system has a good behavior.