Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

Deterioration of sewer pipes is one of very important problems in Japan. Sewer inspections have been carried out mainly by visual check or wired remote robots with a camera. However, such inspection schemes involve high labor and/or monetary cost. Sewer inspection with boat-type video cameras or unwired robots takes a long time to check the result of the inspection because video data are obtained after the equipment is retrieved from the pipe. To realize low cost, safe and quick inspection of sewer pipes, we have proposed a sewer inspection system using drifting wireless sensor nodes. Water, soil, and the narrow space in the pipe make the long-range and high throughput wireless radio communication difficult. Therefore, we have to identify suitable radio frequency and antenna configuration based on wireless communication characteristics in sewer pipes. If the frequency is higher, the Fresnel zone, the needed space for the line of sight is small, but the path loss in free space is large. On the other hand, if the frequency is lower, the size of the Fresnel zone is large, but the path loss in free space is small. We conducted wireless communication experiments using 920MHz, 2.4GHz, and 5GHz band off-the-shelf devices in an experimental underground pipe. The measurement results show that the wireless communication range of 5GHz (IEEE 802.11a) is over 8m in a 200mm-diameter pipe and is longer than 920MHz (ARIB STD-T108), 2.4GHz (IEEE 802.11g, IEEE 802.15.4) band at their maximum transmission power. In addition, we confirmed that devices that use IEEE 802.11a and 54Mbps bit rate can transmit about 43MB data while they are in the communication range of an AP and drift at 1m/s in a 200mm-diameter pipe, and it is bigger than one of devices that use other bit rate.

Energy harvesting wireless sensor networks (EH-WSNs) are being actively studied in order to solve the problems faced by battery-operated WSNs, namely the cost for battery replacement and the negative impact on the environment. In EH-WSNs, each node harvests ambient energy, such as light, heat, vibration, and uses it for sensing, computations, and wireless communications, where the amount of harvested energy of each node varies depending on their environments. MAC protocols for EH-WSNs need to be designed to achieve high throughput and fairness, however, the conventional MAC protocols proposed for EH-WSNs do not adapt to the harvesting rate of each node, resulting in poor fairness. In this paper, we propose a fair MAC protocol based on polling scheme for EH-WSNs. The proposed scheme adjusts contention probability of each node according to its harvesting rate, thereby increasing the throughput of nodes with low harvesting rate. We evaluate throughput and fairness of the proposed fair polling scheme by theoretical analysis and computer simulations, and show that the proposed scheme can improve fairness with little degradation of the overall network throughput.

An overview of the evolution of intelligent transport systems (ITS) supported by advances in information and communication technologies is presented. Focusing on a sensing platform as one of the ITS applications, this paper presents a survey on vehicular ad hoc network-based geographic routing. In addition to the minimum requirement of street-awareness based on street maps, traffic and packet-awareness are considered essential to achieve acceptable packet delivery performance. In particular, in addition to statistical information, real-time traffic and packet level information are indispensable for making routing protocols feasible and effective. Considering traffic conditions that are highly space- and time-dependent, static nodes can be used to assist with geographic routing, and a protocol workable under a partial deployment of static nodes is considered.

Key infection is a lightweight key-distribution protocol for partially compromised wireless sensor networks, where sensor nodes send cryptographic keys in the clear. As the adversary is assumed to be present partially at the deployment stage, some keys are eavesdropped but others remain secret. To enhance the security of key infection, secrecy amplification combines keys propagated along different paths. Two neighbor nodes W1 and W2 can use another node W3 to update their key. If W3 is outside of the eavesdropping region of the adversary, the updated key is guaranteed to be secure. To date, the effectiveness of secrecy amplification has been demonstrated only by simulation. In this article, we present the first mathematical analysis of secrecy amplification. Our result shows that the effectiveness of secrecy amplification increases as the distance between the two neighbor nodes decreases.

In this paper, a multi-data and multi-ACK verified selective forwarding attacks (SFAs) detection scheme is proposed for containing SFAs. In our scheme, each node (in addition to the nodes in the hotspots area) generates multiple acknowledgement (ACK) message for each received packet to confirm the normal packet transmission. In multiple ACK message, one ACK is returned along the data forwarding path, other ACKs are returned along different routing paths, and thus malicious nodes can be located accurately. At the same time, source node send multiple data routing, one is primary data routing, the others are backup data routing. Primary data is routed to sink directly, but backup data is routed to nodes far from sink, and then waits for the returned ACK of sink when primary data is routed to sink. If a node doesn't receive the ACK, the backup data is routed to sink, thus the success rate of data transmission and lifetime can be improved. For this case, the MDMA scheme has better potential to detect abnormal packet loss and identify suspect nodes as well as resilience against attack. Theoretical analysis and experiments show that MDMA scheme has better ability for ensuring success rate of data transmission, detecting SFA and identifying malicious nodes.

Recently, multihop wireless sensor networks (WSNs) are widely developed and applied to energy efficient data collections from environments by establishing reliable transmission radio links and employing data aggregation algorithms, which can eliminate redundant transmissions and provide fusion information. In this paper, energy efficiency which consists of not only energy consumptions but also the amount of received data by the base station, as the performance metric to evaluate network utilities is presented for achieving energy efficient data collections. In order to optimize energy efficiency for improvements of network utilization, we firstly establish a graphical game theoretic model for energy efficiency in multihop WSNs, considering message length, practical energy consumptions and packet success probabilities. Afterwards, we propose a graphical protocol for performance optimization from Nash equilibrium of the graphical game theory. The approach also consists of the distributed protocol for generating optimum tree networks in practical WSNs. The experimental results show energy efficient multihop communications can be achieved by optimum tree networks of the approach. The quantitative evaluation and comparisons with related work are presented for the metric with respect to network energy consumptions and the amount of received data by the base station. The performances of our proposal are improved in all experiments. As an example, our proposal can achieve up to about 52% energy efficiency more than collection tree protocol (CTP). The corresponding tree structure is provided for the experiment.

Energy-efficient tracking of continuous objects such as fluids, gases, and wild fires is one of the important challenging issues in wireless sensor networks. Many studies have focused on electing fewer nodes to report the boundary information of continuous objects for energy saving. However, this approach of using few reporting packets is very sensitive to packet loss. Many applications based on continuous objects tracking require timely and precise boundary information due to the danger posed by the objects. When transmission of reporting packets fails, applications are unable to track the boundary reliably and a delay is imposed to recover. The transmission failure can fatally degrade application performance. Thus, it is necessary to consider just-in-time recovery for reliable continuous object tracking. Nevertheless, most schemes did not consider the reliable tracking to handle the situation that packet loss happen. Recently, a scheme called I-COD with retransmission was proposed to recover lost packets but it leads to increasing both the energy consumption and the tracking latency owing to the retransmission. Thus, we propose a reliable tracking scheme that uses fast recovery with the redundant boundary information to track continuous objects in real-time and energy-efficiently. In the proposed scheme, neighbor nodes of boundary nodes gather the boundary information in duplicate and report the redundant boundary information. Then the sink node can recover the lost packets fast by using the redundant boundary information. The proposed scheme provides the reliable tracking with low latency and no retransmissions. In addition, the proposed scheme saves the energy by electing fewer nodes to report the boundary information and performing the recovery without retransmissions. Our simulation results show that the proposed scheme provides the energy-efficient and reliable tracking in real-time for the continuous objects.

In this paper, a multiple-object tracking approach in large-scale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified.

Wireless Sensor Networks (WSNs) have gained importance with a rapid growth in their applications during the past decades. There has also been a rise in the need for energy-efficient and scalable routing along with the data aggregation protocols for the large scale deployments of sensor networks. The traditional routing algorithms suffer from drawbacks such as the presence of one hop long distance data transmissions, very large or very small clusters within a network at the same moment, over-accumulated energy consumption within the cluster-heads (CHs) etc. The lifetime of WSNs is also decreased due to these drawbacks. To overcome them, we have proposed a new method for the Multi-Hop, Far-Zone and Load-Balancing Hierarchical-Based Routing Algorithm for Wireless Sensor Network (MFLHA). Various improvements have been brought forward by MFLHA. The first contribution of the proposed method is the existence of a large probability for the nodes with higher energy to become the CH through the introduction of the energy decision condition and energy-weighted factor within the electing threshold of the CH. Secondly, MFLHA forms a Far-Zone, which is defined as a locus where the sensors can reach the CH with an energy less than a threshold. Finally, the energy consumption by CHs is reduced by the introduction of a minimum energy cost method called the Multi-Hop Inter-Cluster routing algorithm. Our experimental results indicate that MFLHA has the ability to balance the network energy consumption effectively as well as extend the lifetime of the networks. The proposed method outperforms the competitors especially in the middle range distances.

Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization operator and an intelligent allocation operator. A directed perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to speed up the convergence. In addition, with a new geometric sensor detection model instead of the old probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively.Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value.

Because accurate position information plays an important role in wireless sensor networks (WSNs), target localization has attracted considerable attention in recent years. In this paper, based on target spatial domain discretion, the target localization problem is formulated as a sparsity-seeking problem that can be solved by the compressed sensing (CS) technique. To satisfy the robust recovery condition called restricted isometry property (RIP) for CS theory requirement, an orthogonalization preprocessing method named LU (lower triangular matrix, unitary matrix) decomposition is utilized to ensure the observation matrix obeys the RIP. In addition, from the viewpoint of the positioning systems, taking advantage of the joint posterior distribution of model parameters that approximate the sparse prior knowledge of target, the sparse Bayesian learning (SBL) approach is utilized to improve the positioning performance. Simulation results illustrate that the proposed algorithm has higher positioning accuracy in multi-target scenarios than existing algorithms.

Cognitive radio sensor networks (CRSNs) with their dynamic spectrum access capability appear to be a promising solution to address the increasing challenge of spectrum crowding faced by the traditional WSN. In this paper, through maximizing the utility index of the CRSN, a node density-adaptive spectrum access strategy for sensor nodes is proposed that takes account of the node density in a certain event-driven region. For this purpose, considering the burst real-time data traffic, we analyze the energy efficiency (EE) and the packet failure rate (PFR) combining network disconnected rate (NDR) and packet loss rate (PLR) during the channel switching interval (CSI) for both underlay and interweave spectrum access schemes. Numerical results confirm the validity of our theoretical analyses and indicate that the adaptive node density threshold (ANDT) exists for underlay and interweave spectrum access scheme switching.

The recently proposed distributed adaptive direct position determination (D-ADPD) algorithm provides an efficient way to locating a radio emitter using a sensor network. However, this algorithm may be suboptimal in the situation of colored emitted signals. We propose an enhanced distributed adaptive direct position determination (EDA-DPD) algorithm. Simulations validate that the proposed EDA-DPD outperforms the D-ADPD in colored emitted signals scenarios and has the similar performance with the D-ADPD in white emitted signal scenarios.

This paper empirically validates battery-less sensor activation via wireless energy transmission to release sensors from wires and batteries. To seamlessly extend the coverage and activate sensor nodes distributed in any indoor environment, we proposed multi-point wireless energy transmission with carrier shift diversity. In this scheme, multiple transmitters are employed to compensate path-loss attenuation and orthogonal frequencies are allocated to the multiple transmitters to avoid the destructive interference that occurs when the same frequency is used by all transmitters. In our previous works, the effectiveness of the proposed scheme was validated theoretically and also empirically by using just a spectrum analyzer to measure the received power. In this paper, we develop low-energy battery-less sensor nodes whose consumed power and required received power for activation are respectively 142µW and 400µW. In addition, we conduct indoor experiments in which the received power and activation of battery-less sensor node are simultaneously observed by using the developed battery-less sensor node and a spectrum analyzer. The results show that the coverage of single-point and multi-point wireless energy transmission without carrier shift diversity are, respectively, 84.4% and 83.7%, while the coverage of the proposed scheme is 100%. It can be concluded that the effectiveness of the proposed scheme can be verified by our experiments using real battery-less sensor nodes.

A partitioning parallelization of the multi-objective evolutionary algorithm based on decomposition, pMOEA/D, is proposed in this letter to achieve significant time reductions for expensive bi-objective optimization problems (BOPs) on message-passing clusters. Each sub-population of pMOEA/D resides on a separate processor in a cluster and consists of a non-overlapping partition and some extra overlapping individuals for updating neighbors. Additionally, sub-populations cooperate across separate processors by the hybrid migration of elitist individuals and utopian points. Experimental results on two benchmark BOPs and the wireless sensor network layout problem indicate that pMOEA/D achieves satisfactory performance in terms of speedup and quality of solutions on message-passing clusters.

In tree-based wireless sensor networks (WSNs), multihop sensor nodes require a longer time frame to send sensed data to a sink node as the number of hops increases. The time taken for delivery of sensed data becomes a critical issue when a large WSN is deployed. This paper proposes a new data collection scheme with rapid data delivery that utilizes the so-called mobile agent technique. The proposed scheme achieves high data collection efficiency while not relying on route optimization unlike conventional data collection techniques. Simulation results show that the larger the size or the maximum hops of the network, the more effective the proposed scheme becomes. Effectiveness of the proposed scheme is also confirmed through field experiments with actual sensor devices.

Accidental falling among elderly people has become a public health concern. Thus, there is a need for systems that detect a fall when it happens. This paper presents a portable real-time remote health monitoring system that can remotely monitor patients' movements. The system is designed and implemented using ZigBee wireless technologies, and the data is analysed using Matlab. The purpose of this research is to determine the acceleration thresholds for fall detection, using tri-axial accelerometer readings at the head, waist, and knee. Seven voluntary subjects performed purposeful falls and Activities of Daily Living (ADL). The results indicated that measurements from the waist and head can accurately detect falls; the sensitivity and reliability measurements of fall detection ranged between 80% and 90%. In contrast, the measurements showed that the knee is not a useful position for the fall detection.

To design a wireless sensor network for farms, it is necessary to understand and predict the effect of vegetation. In this study, the change in the propagation loss characteristics in 920-MHz band is examined during the growth of mulberry bushes. The received signal strength indicator (RSSI) is measured as a function of the distance between the transmitting antenna (Tx) and the receiving antenna (Rx) in a 50×50m mulberry field. The Tx and Rx are placed at a height of 1.5m. Moreover, the horizontal and vertical polarizations are measured and the differences are shown. Three empirical vegetation attenuation models are introduced, and the measured data have been fitted to each model. The results show that the non-zero gradient model is the best model at predicting the vegetation attenuation in a mulberry farm regardless of the polarization or mulberry growth. It is found that the attenuation dependence on the plant height is linear. Furthermore, the results have revealed that the horizontal polarization had about 1.5 times as large an effect on the vegetation attenuation as the vertical polarization.

Time synchronization is of paramount importance in wireless sensor networks (WSNs) due to the inherent distributed characteristics of WSNs. Border surveillance WSNs, especially, require a highly secure and accurate time synchronization scheme to detect and track intruders. In this paper, we propose a Secure and Efficient Time synchronization scheme for Border surveillance WSNs (SETB) which meets the requirements of border surveillance WSNs while minimizing the resource usage. To accomplish this goal, we first define the performance and security requirements for time synchronization in border surveillance WSNs in detail. Then, we build our time synchronization scheme optimized for these requirements. By utilizing both heterogeneous WSNs and one-way key chains, SETB satisfies the requirements with much less overhead than existing schemes. Additionally, we introduce on-demand time synchronization, which implies that time synchronization is conducted only when an intruder enters the WSN, in order to reduce energy consumption. Finally, we propose a method of deploying time-source nodes to keep the synchronization error within the requirement. Our analysis shows that SETB not only satisfies the performance and security requirements, but also is highly efficient in terms of communication and computation overhead, thus minimizing energy consumption.