TinyOS Extensions for a Wireless Sensor Network Node Based on a Dynamically Reconfigurable Processor

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

The use of wireless multimedia sensor networks (WMSNs) for surveillance applications has attracted the interest of many researchers. As with traditional sensor networks, it is easy to deploy and operate WMSNs. With inclusion of multimedia devices in wireless sensor networks, it is possible to provide data to users that is more meaningful than that provided by scalar sensor-based systems alone; however, producing, storing, processing, analyzing, and transmitting multimedia data in sensor networks requires consideration of additional constraints, including energy, processing power, storage capacity, and communication. Furthermore, as multimedia sensors produce much more data than scalar sensors, more manpower is required to analyze multimedia data. To overcome these constraints and challenges, this paper aimed to propose a system architecture and a set of procedures for WMSNs that facilitate automatic classification of moving objects using scalar and multimedia sensors. Methods and standards for detecting and classifying a moving object, as well as transmission of the results, are described in detail. The hardware for each sensor node includes a built-in camera, a passive infrared motion sensor, a vibration sensor, and an acoustic sensor. An application using our proposed methods was developed and embedded in the multimedia sensor node. In addition, a sink station was set up and the data produced by the sensor network was collected by this server. The classification performance of the application was tested using video recorded by the sensor node. The effect of the proposed methods on power consumption was also tested and measured. The experimental results show that the proposed approach is sufficiently lightweight to be used for real-world surveillance applications.

An embedded sensor network is a system of nodes, each of which is equipped with a certain amount of sensing, actuating, computation, communication and storage components. Two major components of sensor nodes are sensing unit and wireless transceiver. They directly interact with nodes in wireless sensor networks (WSNs) that are easily prone to failure due to hardware failure, communication link errors, energy depletion, malicious attacks, etc. Even if the sensor node hardware is in excellent condition, still the communication between sensor nodes depends on many factors such as signal strength, obstacles and interference. Degradation in these factors results in low reliability of sensor nodes. One of the key prerequisite for an effective, efficient embedded sensor network is utilization of low-cost, low-overhead and high-resilient fault-inference techniques. Our attempt is to address fault-inference issues in sensor networks using an agent-based approach. Our proposed approach involves an intelligent agent that outfitted with a fault-inference engine. This engine profits from Expectation Maximization (EM) algorithm to evaluate fault probabilities of sensor nodes. Our experiment in a wireless sensor testbed is conducted to demonstrate the correctness and effectiveness of our approach.

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

Wireless Sensor Network is greatly evolved in recent years. Technological advancements in wireless networks are intended to develop various fields especially in medical domain. Nowadays, remote health monitoring is possible by the enormous growth of wireless body area sensor networks. The Wireless Body Area Sensor Network monitors the human health by using wearable body sensors, and sends the status of the human health to the medical experts. Body nodes will be placed on, in and around the human body. The major key issue in Wireless Body Area Sensor Network is power management. Since batteries used in sensors are very tiny, it tends to have a minimal lifetime. In order to increase the lifetime of the sensor node, the energy needs to be utilized in an efficient manner. In this paper we have proposed conditional re-transmission technique to minimize the energy consumption. So the sensor nodes lifetime will get increased and in turn Wireless Body Area Sensor Networks lifetime will also be increased. By increasing the lifetime of the sensor nodes, the batteries or sensor nodes need not to be replaced frequently.

Wireless connection based smart sensors network can combine sensing, computation, and communication into a single, small device.Because sensor carries its own wireless data transceiver, the time and the cost for construction, maintenance, the size and weight of whole system have been reduced.Information collected from these sensor nodes is routed to a sink node via different types of wireless communication approaches.Healthcare systems have restricted the activity area of patients to be within the medical health care center or residence area.To provide more a feasible situation for patients, it is necessary to embed wireless communication technology into healthcare systems.The physiological signals are then immediately transmitted to a remote management center for analysis using wireless local area network.Healthcare service has been further extended to become mobile care service due to the ubiquity of global systems for mobile communications and general packet radio service.It is important that using sensors to detect field-environment signals in agriculture is understood since a long time ago.Precision agriculture is a technique of management of large fields in order to consider the spatial and temporal variability.To use more sophisticated sensor devices with capabilities of chemical and biological sensing not only aids the personnel in the field maintenance procedure but also significantly increases the quality of the agricultural product.In this chapter, we examine the fields in healthcare and precision agriculture based on wireless sensor networks.In the application of healthcare systems, a System on a Chip (SoC) platform and Bluetooth wireless network technologies were combined to construct a wireless network physiological signal monitoring system.In the application of precision agriculture, an SoC platform was also used combining the ZigBee technology to consist a field signals monitoring system.In addition to the two applications, the fault tolerance in wireless sensor networks is also discussed in this chapter.

This thesis addresses the problem of traffic transfer in wireless sensor networks (WSN). In such networks, the foremost challenge in the design of data communication techniques is that the sensor's transceiver circuitry consumes the major portion of the available power. Thus, due to stringent limitations on the nodes' hardware and power resources in WSN, data transmission must be power-efficient in order to reduce the nodes' power consumption, and hence to maximize the network lifetime while satisfying the required data rate. The transmit power is itself under the influence of data rate and source-destination distance. Thanks to the dense deployment of nodes in WSN, multi-hop communication can be applied to mitigate the transmit power for sending bits of information, i.e., gathered data by the sensor nodes to the destination node (gateway) compared to single-hop scenarios. In our approach, we achieve a reasonable trade-off between power-efficiency and transmission data rate by devising cooperative communication strategies through which the network traffic (i.e. nodes' gathered information) is relayed hop-by-hop to the gateway. In such strategies, the sensor nodes serve as data originator as well as data router, and assist the data transfer from the sensors to the gateway. We develop several data transmission schemes, and we prove their capability in transmitting the data from the sensor nodes at the highest possible rates allowed by the network limitations. In particular, we consider that (i) network has linear or quasi-linear topology, (ii) nodes are equipped with half-duplex radios, implying that they cannot transmit and receive simultaneously, (iii) nodes transmit their traffic at the same average rate. We compute the average data rate corresponding to each proposed strategy. Next, we take an information-theoretic approach and derive an upper bound to the achievable rate of traffic transfer in the networks under consideration, and analyze its tightness. We show that our proposed strategies outperform the conventional multi-hop scheme, and their average achievable rate approaches the upper bound at low levels of signal to noise ratios

In recent years, a new wave of networks labelled Wireless Sensor Networks (WSNs) has attracted a lot of attention from researchers in both academic and industrial communities. A WSN consists of a collection of sensor nodes and a base station connected through wireless channels, and can be used for many applications such as military application, building distributed systems, physical environment monitoring, and security surveillance among others. A big advantage of sensor networks is represented by ease of deployment, reducing installation cost, possibility to distribute the tiny sensors over a wide region, and larger fault tolerance (V. Loscri et al., 2005). However, despite the infinite scopes of wireless sensor networks applications, they are limited by the node battery lifetime. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks and necessitate energyawareness at all layers of the networking protocol stack (Q. Xue & A. Ganz, 2004). Therefore, energy efficient algorithms have been one of the most challenging issues for WSNs. Sensor nodes can be in one of four states, namely transmit, receive, idle and sleep. The largest part of a node’s energy is consumed while transmitting and receiving. Minimizing the number of communications by eliminating or aggregating redundant sensed data saves much amount of energy (L. B. Ruiz et al., 2003). Among these clustering sensor networks are a very attractive approach because clustering allows for scalability, data aggregation, and energy efficiency. In a clustering network, nodes are grouped into clusters and there are special nodes called cluster head. They are responsible for an efficient way to lower energy consumption within a cluster by performing data aggregation. In a heterogeneous sensor network, two or more different types of nodes with different battery energy and functionality are used. On the other hand, in homogeneous networks all the sensor nodes are identical in terms of battery energy and hardware complexity. As a result, network performance decreases since the cluster head nodes goes down before other nodes do. Thus dynamic, energy efficient and adaptive cluster head selection algorithm is very important. Sensor networks can be divided in two classes as event-driven and continuous dissemination networks according to the periodicity of communication (L. B. Ruiz et al., 9

In large-scale Wireless Sensor Networks (WSNs) the amount of data gathered require energy efficient data dissemination and data retrieval techniques. Data Centric Sensor (DCS) networks is a better approach in which the sensed data are sent to a sensor node whose name is associated with sensed data. Due to unattended nature of Wireless Sensor Networks, these sensor nodes are susceptible to different types of attacks. In this paper we propose a Secure Data Centric Sensor (SDCS) Networks that includes security and privacy support to DCS networks. In addition, we propose a multi-query optimization technique that aggregates similar queries and reduces the number of messages. Simulation and experimental results show that our work provides a secure data centric sensor network based on cryptographic keys and reduces the message overhead and incurs a minimum communication cost compared to previous works.

Node placement for connected target coverage in wireless sensor networks with dynamic sinks

Sensor network simulators are important tools for the design, implementation and evaluation of wireless sensor networks. Due to the large computational requirements necessary for simulating wireless sensor networks with high fidelity, many wireless sensor network simulators, especially the cycle accurate ones, employ distributed simulation techniques to leverage the combined resources of multiple processors or computers. However, the large overheads in synchronizing sensor nodes during distributed simulations of sensor networks result in a significant increase in simulation time. In this paper, we present a novel technique that could significantly reduce such overheads by minimizing the number of sensor node synchronizations during simulations. We implement this technique in Avrora, a widely used parallel sensor network simulator, and achieve a speedup of up to 11 times in terms of average simulation speed in our test cases. For applications that have lower duty cycles, the speedups are even greater since the performance gains are proportional to the sleep times of the sensor nodes.

One important issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is a scarce resource in a sensor node. Many protocols have been proposed for data-gathering between wireless sensor nodes. However, most of these protocols work on static wireless sensor networks. In this paper, we provide clustering-based and time-driven protocols which minimize energy dissipation for data-gathering in wireless mobile sensor networks where the sensor nodes are capable of mobility. We first consider the node mobility when organizing clusters. Our protocols will have a sensor node select a proper cluster-head to join in order to save energy. We then consider how to elect the cluster-heads and provide two algorithms for cluster-head election. Last, we implement all the protocols and perform the experiments for evaluating the protocols.

With the aging of the global population there is a growing number of elderly people needing care with proportionally fewer caregivers and lesser resources to support them. While many older people move into a managed care environment, there is a significant portion that, for various reasons, chooses to live alone. A system has been conceived and developed to provide assistance in caring for the elderly living alone that uses a network of wireless sensors to monitor the appliance usage of such a person. In order to detect the usage of appliances the sensor nodes measure the current that the appliance is using and report this information to a master node via a wireless communication link. If the usage pattern does not fit within a set of predefined rules an alarm can be raised. While such a system has been tested as a proof of concept system, this work looks to miniaturise the sensor nodes and the master unit so that they can be more easily used and accepted in a domestic environment. The required functionality for the sensor nodes includes a current sensor, wireless transceiver, power supply, microcontroller and user interface. The evaluation of suitable current sensors has been done and the details of the final hardware and software designs are presented. The design of the sensor and master node firmware is outlined and the development of the PC based software that controls the system is presented.

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

Wireless sensor networks have received a lot of attention in recent years due to their wide spectrum of applications. Localization a technique used in ubiquitous sensor networks. Most localization techniques apply RSSI-based ranging techniques to compute the location of the object in a wireless sensor network. However, a wireless sensor network is a fading-signal environment that has noise, which causes RSSI to become unstable and leads to abrupt distance estimates. In this paper, we propose the Verification-Based Localization Method (VBLM) to alleviate the effect of unstable signals to provide high-accuracy location estimates in wireless sensor environments. VBLM filters noisy signals in the localization process. The sensor node uses a neighboring beacon to help verify the quality of received signal under acceptable communication cost. Noisy signals can thus be removed to decrease localization error. A set of experiments was conducted in an outdoor environment. The experimental results show that VBLM reduces the localization error in unstable signal sensor networks better than is possible with other localization methods.