Wireless Sensor Networks

Duty-cycling is an appealing solution for energy savings in densely deployed, energy-constrained wireless sensor networks (WSNs). Indeed, several applications, such as intruder detection and tracking, require the design of k-covered WSNs, which are densely in nature and where each location in a monitored field is covered (or sensed) by at least k active sensors. With duty-cycling, sensors can be turned on or off according to a scheduling protocol, thus reducing the number of active sensors required to k-cover a field and helping all sensors deplete their energy slowly and uniformly. In this paper, we propose a duty-cycling framework, called clustered randomized m-connected k-coverage (CRACC _mk ), for k-coverage of a sensor field. We present two protocols using CRACC _mk , namely T-CRACC _mk and D-CRACC _mk , which differ by their degree of granularity of network clustering. We prove that the CRACC _mk protocols are minimum energy m-connected k-coverage protocols in that each deploys a minimum number of active sensors to k-cover a sensor field and that k-coverage implies m-connectivity between all active sensors, with m being larger than k. We enhance the practicality of the CRACC _mk protocols by relaxing some widely used assumptions for k-coverage. Simulation results show that the CRACC _mk protocols outperform existing k-coverage protocols for WSNs.

A localization algorithm using radio interferometric measurements is presented. A probabilistic model is constructed that accounts for general noise models and lends itself to distributed computation. A message passing algorithm is derived that exploits the geometry of radio interferometric measurements and can support sparse network topologies and noisy measurements. Simulations on real and simulated data show promising performance for 2D and 3D deployments.

Precise localization of mobile objects is a common problem in WSN research for which various approaches exist. However, apart from technical aspects and the location estimation itself, speed, reliability and energy efficiency are central but barely addressed aspects within such systems.

In this paper, we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show that the Bayesian Network model can learn cyclical baselines for gas concentrations, thus reducing false alarms usually caused by flatline thresholds. Further, we show that the system can learn dependencies between changes of concentration in different gases and at multiple locations. We define and identify new types of events that can occur in a sensor network. In particular, we analyse joint events in a group of sensors based on learning the Bayesian model of the system, contrasting these events with merely aggregating single events. We demonstrate that anomalous events in individual gas data might be explained if considered jointly with the changes in other gases. Vice versa, a network-wide spatiotemporal anomaly may be detected even if individual sensor readings were within their thresholds. The presented Bayesian approach to spatiotemporal anomaly detection is applicable to a wide range of sensor networks.

We examine the problem of tracking dynamic boundaries occurring in natural phenomena using range sensors. Two main challenges of the boundary tracking problem are energy-efficient boundary estimations from noisy observations and continuous tracking of the boundary. We propose a novel approach which uses a regression-based spatial estimation technique to determine discrete points on the boundary and estimates a confidence band around the entire boundary. In addition, a Kalman Filter-based temporal estimation technique is used to selectively refresh the estimated boundary to meet the accuracy requirements. Our algorithm for dynamic boundary tracking (DBTR) combines temporal estimation with an aperiodically updated spatial estimation and provides a low overhead solution to track boundaries without requiring prior knowledge about the dynamics of the boundary. Experimental results demonstrate the effectiveness of our algorithm and estimated confidence bands achieve loss of coverage of less than 2 − 5% for a variety of boundaries with different spatial characteristics.

In Wireless Sensor Networks large number of nodes and limited energy available per node calls for designing efficient transmission protocols. Cooperative transmission is one of the protocols which helps wireless nodes to achieve spatial diversity, which translates into reduction in transmission power or increase in coverage area. Cooperative protocol can be realized with or without (called conventional afterward) network coding; and the network-coding-based (respectively the conventional) protocol can be operated in either static or adaptive manner. For an efficient operation of cooperative protocols, good quality inter-source channels are required, which in turn depend on relative location of nodes within a network. In this work, a three-node cooperative network consisting of source, relay, and destination nodes is considered. At high signal-to-noise ratio values, we first approximate the outage probability result when the network-coding-based adaptive protocol is implemented. Then, based on the approximate probability result, a diversity-multiplexing tradeoff is studied; the result shows that this protocol performs similar to an amplify-and-forward protocol. Next, for the various protocols, the coverage area and relative location of the relay that minimizes the outage are studied; for that the exact outage probability results are used. Over wider geographic area, network-coding-based static and adaptive protocols perform better than their conventional counterparts, and this happens when the relaying node is positioned closer to the destination than the source. The conventional protocols perform better when the relay is positioned closer to the source. In Wireless Sensor Networks, assuming that relay nodes which are closer to both the source and destination exist, these results help as a guide in selecting with which node to cooperate (relay selection) when one cooperative scheme is implemented.

Storing and disseminating coded information instead of the original data can bring significant performance improvements to sensor network protocols. Such methods reduce the risk of having some data replicated at many nodes, whereas other data is very scarce. This is of particular importance for data persistence in sensor networks. While coding is generally beneficial, coding over all available packets can be detrimental to performance, since coded information might not be decodable after a network failure. In this paper we investigate the suitability of different codeword degree distributions with respect to the dynamics of the underlying wireless network and design a corresponding data management algorithm. We further propose a simple buffer management scheme for continuous data gathering. The performance of the protocols is demonstrated by means of simulation, as well as experiments with an implementation on MICAz motes.

Future mobile sensing systems are being designed using 802.15.4 low-power short-range radios for a diverse set of devices from embedded mobile motes to sensor-enabled cellphones in support, for example, of people-centric sensing applications. However, there is little known about the use of 802.15.4 in mobile sensor settings nor its impact on the performance of future communication architectures. We present a set of initial results from a simple yet systematic set of benchmark experiments that offer a number of important insights into the radio characteristics of mobile 802.15.4 person-to-person communication. Our results show that the body factor - that is to say, the human body and where sensors are located on the body (e.g., on the chest, foot, in the pocket) - has a significant effect on the performance of the communications system. While this phenomenon has been discussed in the context of other radios (e.g., cellular, WiFi, UWB) its impact on 802.15.4 based mobile sensor networks is not understood. Other findings that also serve to limit the communication performance include the effective contact times between mobile nodes, and, what we term the zero bandwidth crossing, which is a product of mobility and the body factor. This paper presents a set of initial findings and insights on this topic, and importantly, we consider the impact of these findings on the design of future communication architectures for mobile sensing.

Although formerly conceived for industrial sensing and control over Wireless Sensor Networks, LR-WPANs are registering an increasing interest in experimenting multimedia applications, with particular emphasis on evaluating the streaming capability of Zigbee networks. Due to their limited throughput they are not expected to provide high QoS, nevertheless there are several application scenarios such as distributed surveillance, emergency and rescue where audio and video streaming over low cost Zigbee networks is highly desirable. In this paper we first investigate the feasibility of Zigbee-like networks for low-rate voice streaming applications. We analyze important streaming metrics such as throughput, packet loss and jitter in multi-hop topologies. We propose some improvements in the stack implementation and show the performance in order to determine the streaming capacity limits of LR-WPAN networks.

The mass casualty emergency response involves logistic impediments like overflowing victims, paper triaging, extended victim wait time and transport. We propose a new system based on a location aware wireless sensor network (WSN) to overcome these impediments and assists the emergency responders (ER) in providing efficient emergency response. We have developed a ZigBeeready acceleration sensor node hardware which is energy efficient as shown by its current consumption results. ZigBee mesh network is setup and a RSSI-based localization solution is analyzed. The main functionality of this WSN is to collect real time data - patient/emergency doctor tracking, triage information, patient vital signs/activity and communicate it to the ER’ Monitor Station device that runs the visualization software. We have implemented this software using the new ‘Care Zone Count Algorithm’ a dynamic mechanism based on localized events and data acquired from the WSN. This algorithm calculates and displays the patient count in each care zone, victim flow rate, transport capacity, thereby enabling the ER to efficiently estimate the resources required. The analysis of this algorithm verifies that the proposed system creates situation awareness to the ER.

Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time.

The main objective of this paper is to provide a useful fully-distributed inference algorithm for clustering, based on belief propagation. The algorithm selects cluster heads, based on a unique set of global and local parameters, which finally achieves, under the energy constraints, improved network performance. Evaluation of the algorithm implementation shows an increase in throughput in more than 40% compared to HEED scheme. This advantage is expressed in terms of network reliability, data collection quality and transmission cost.

We consider the problem of determining the transmission power assignment that maximizes the lifetime of a data-gathering wireless sensor network with stationary nodes and static transmission power levels. We present a simple and efficient distributed algorithm for this task that works by establishing the minimum power level at which the network stays connected. The algorithm is based on a binary search over the range of feasible transmission power levels and does not require prior knowledge of network topology. We study the performance of the resulting BSpan protocol by network simulations and compare the number of control messages required by BSpan to two other recently proposed methods, the Distributed Min-Max Tree (DMMT) and Maximum Lifetime Spanner (MLS) algorithms. We find that BSpan outperforms both DMMT and MLS significantly.

In a Wireless Sensor Network, sensor nodes may fail for several reasons and the network may split into two or more disconnected partitions. This may deteriorate or even nullify the usefulness and effectiveness of the network. Therefore, repairing partitions is a priority. In this paper we present a method to repair network partitions by using mobile nodes. By reasoning upon the degree of connectivity with neighbours, a mobile node finds the proper position where to stop in order to re-establish connectivity. Factors influencing the method performance are singled out and criteria for their selection are discussed. Simulations show that the proposed method is effective and efficient notwithstanding packet loss.

We present Typhoon, a protocol designed to reliably deliver large objects to all the nodes of a wireless sensor network (WSN). Typhoon uses a combination of spatially-tuned timers, prompt retransmissions, and frequency diversity to reduce contention and promote spatial re-use. We evaluate the performance benefits these techniques provide through extensive simulations and experiments in an indoor testbed. Our results show that Typhoon is able to reduce dissemination time and energy consumption by up to three times compared to Deluge. These improvements are most prominent in sparse and lossy networks that represent real-life WSN deployments.

Wireless Sensor Networks (WSNs) are increasingly being proposed in scenarios whose requirements cannot be fully predicted, or where the system functionality must adapt to changing conditions. In these scenarios, the ability to reconfigure portions of the software running on WSN nodes becomes imperative. At the same time, recent WSN proposals often employ heterogeneous nodes (e.g., sensors and actuators), which require the deployment of different code on different devices, based on their characteristics. Unfortunately, existing work in the field largely focuses on simpler scenarios where the same, monolithic program is distributed to all the nodes in the WSN.

In this paper we present FiGaRo, a programming model supported by an efficient run-time system and distributed protocols, collectively enabling an unprecedented fine-grained control over what is being reconfigured, and where. Using FiGaRo, the programmer can deal explicitly with component dependencies and version constraints, as well as select precisely the subset of nodes targeted by reconfiguration, leaving the others unaltered. We show that our run-time support imposes a very limited processing and memory overhead, while the communication overhead lies within 9% of the theoretical optimum.

By using Elliptic Curve Cryptography (ECC), it has been recently shown that Public-Key Cryptography (PKC) is indeed feasible on resource-constrained nodes. This feasibility, however, does not necessarily mean attractiveness, as the obtained results are still not satisfactory enough. In this paper, we present results on implementing ECC, as well as the related emerging field of Pairing-Based Cryptography (PBC), on two of the most popular sensor nodes. By doing that, we show that PKC is not only viable, but in fact attractive for WSNs. As far as we know pairing computations presented in this paper are the most efficient results on the MICA2 (8-bit/7.3828-MHz ATmega128L) and Tmote Sky (16-bit/8.192-MHz MSP-430) nodes.

Sensors networks instrument the physical space using motes that run network embedded programs thus acquiring, processing, storing and transmitting sensor data. The motes commercially available today are large, costly and trade performance for flexibility and ease of programming. New generations of motes are promising to deliver significant improvements in terms of power consumption and price — in particular motes based on System-on-a-chip. The question is how do we compare mote performance? How to find out which mote is best suited for a given application? In this paper, we propose a vector-based methodology for benchmarking mote performance. Our method is based on the hypothesis that mote performance can be expressed as the scalar product of two vectors, one representing the mote characteristics, and the other representing the application characteristics. We implemented our approach in TinyOS 2.0 and we present the details of our implementation as well as the result of experiments obtained on commercial motes from Sensinode. We give a quantitative comparison of these motes, and predict the performance of a data acquisition application.

Several considerable impediments stand in the way of sensor network prototype applications that wish to realize sustained deployments. These are: scale, longevity, data of interest, and infrastructure integration. We present a tool, Que, which assists those sensor network deployments transitioning from prototypes to early production environments by addressing these issues. Que is able to simulate realistic deployments with faithful data, provide fast and iterative feedback on operations, and compose applications quickly in a platform-independent manner. We demonstrate Que’s applicability via tests against our new data center environment-monitoring deployment, DataCenter.NET.

To support the increasing number of sensor devices with various characteristics and requirements, sensor network operating systems should provide an appropriate device driver model that can cover a wide range of device types. Unfortunately, current sensor network operating systems force the user to build complex drivers for even simple devices, provide restricted interfaces, or do not provide any mechanisms. We present a device driver model that is flexible enough to support both simple devices with simple drivers, and complex devices with portable and high-performance device drivers. Users can write a device driver for simple devices with only a few lines of code using the user-mode device driver. Devices that need highly efficient code or portability can be supported by a single-layer or 2-layer kernel-mode device driver. Moreover, shared access and power management can easily be included in the device driver using the device manager. We also provide guidelines for choosing a proper device driver model with concrete examples of real-world devices and support our claims through the evaluation of the device driver model using the RETOS kernel.