Energy efficient and QoS based routing protocol for wireless sensor networks

In recent years there has been several technological advances in Wireless Sensor Networks (WSN), but energy still remains a paramount resource. The amount of available energy has a direct effect on the performance, functionality and lifetime of WSN. Being bound by cost and size, sensor nodes are usually equipped with limited amount of energy and therefore requires a replacement of batteries occasionally. But replacement might not always be feasible option and in some scenarios might even be prohibitive. This indicates the need for more viable solutions, these involve generating energy at the sensor nodes or have it delivered to them i.e., energy harvesting or wireless energy transfer. The objective of this paper is threefold: first we present a survey on potential renewable energy resources along with their characteristics and applications in WSN. Second, this study also describes various battery recharging techniques and their applications with respect to WSN. Finally, we discuss formidable issues, challenges and future research directions.

Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review

Data collection is a crucial operation in wireless sensor networks. The design of data collection schemes is challengingdue to the limited energy supply and the hot spot problem. Leveraging empirical observations that sensory data possess strongspatiotemporal compressibility, this paper proposes a novel compressive data collection scheme for wireless sensor networks. We adopt a power-law decaying data model verified by real data sets and then propose a random projection-based estimation algorithm for this data model. Our scheme requires fewer compressed measurements, thus greatly reduces the energy consumption. It allowssimple routing strategy without much computation and control overheads, which leads to strong robustness in practical applications. Analytically, we prove that it achieves the optimal estimation error bound. Evaluations on real data sets (from the GreenOrbs, IntelLab and NBDC-CTD projects) show that compared with existing approaches, this new scheme prolongs the network lifetime by $1.5 \times$ to $2 \times$ for estimation error 5-20 percent.

/pdf/cdc-compressive-data-collection-for-wireless-sensor-networks-4c7swnn2br.pdf

CDC : Compressive Data Collection for Wireless Sensor Networks

Wearables and mobile devices see the world through the lens of half a dozen low-power sensors, such as, barometers, accelerometers, microphones and proximity detectors. But differences between sensors ranging from sampling rates, discrete and continuous data or even the data type itself make principled approaches to integrating these streams challenging. How, for example, is barometric pressure best combined with an audio sample to infer if a user is in a car, plane or bike? Critically for applications, how successfully sensor devices are able to maximize the information contained across these multi-modal sensor streams often dictates the fidelity at which they can track user behaviors and context changes. This paper studies the benefits of adopting deep learning algorithms for interpreting user activity and context as captured by multi-sensor systems. Specifically, we focus on four variations of deep neural networks that are based either on fully-connected Deep Neural Networks (DNNs) or Convolutional Neural Networks (CNNs). Two of these architectures follow conventional deep models by performing feature representation learning from a concatenation of sensor types. This classic approach is contrasted with a promising deep model variant characterized by modality-specific partitions of the architecture to maximize intra-modality learning. Our exploration represents the first time these architectures have been evaluated for multimodal deep learning under wearable data -- and for convolutional layers within this architecture, it represents a novel architecture entirely. Experiments show these generic multimodal neural network models compete well with a rich variety of conventional hand-designed shallow methods (including feature extraction and classifier construction) and task-specific modeling pipelines, across a wide-range of sensor types and inference tasks (four different datasets). Although the training and inference overhead of these multimodal deep approaches is in some cases appreciable, we also demonstrate the feasibility of on-device mobile and wearable execution is not a barrier to adoption. This study is carefully constructed to focus on multimodal aspects of wearable data modeling for deep learning by providing a wide range of empirical observations, which we expect to have considerable value in the community. We summarize our observations into a series of practitioner rules-of-thumb and lessons learned that can guide the usage of multimodal deep learning for activity and context detection.

/pdf/multimodal-deep-learning-for-activity-and-context-xetb2jzl3v.pdf

Multimodal Deep Learning for Activity and Context Recognition

On security challenges and open issues in Internet of Things

In this position paper, we investigate the use of wireless sensor network (WSN) technology for ground surveillance. The goal of our project is to develop a prototype of WSN for outdoor deployment. We aim to design a system, which can detect and classify multiple targets (e.g., vehicles and troop movements), using inexpensive off-the-shelf wireless sensor devices, capable of sensing acoustic and magnetic signals generated by different target objects. In order to archive our goals, we intend to design a system, which is capable of automatic selforganization and calibration. Such a system would need to be capable of performing detection and tracking of targets as well as sending the real time enemy mobility information to a command centre. Real-time tacking with WSN is extremely challenging since it requires high system robustness, real time decision making, high frequency sampling, multi-modality of sensing, complex signal processing and data fusion, distributed coordination and wide area coverage. We propose a Hybrid Sensor Network architecture (HSN), tailored specifically to meet these challenges. We investigate data fusion technologies such as particle filters, to handle both environmental and sensing noises of inexpensive sensors.

/pdf/wireless-sensor-networks-for-battlefield-surveillance-1zin7x7gs0.pdf

Wireless Sensor Networks for Battlefield Surveillance

For widespread adoption of sensor technology, robustness in the event of abnormal behavior, such as a network intrusion or failures of components or nodes, is critical. Current research on robust and resilient sensor networking is focused on specific tasks - secure broadcast, secure aggregation, secure localization or fault-tolerant feature extraction. While these primitives provide useful functionality, what has been lacking is a comprehensive, holistic approach to sensor network robustness across various failure modalities. We propose a self-healing hybrid sensor network architecture, called SASHA, that is inspired by and coopts several mechanisms used by the acquired natural immune system to attain its autonomy, robustness, diversity and adaptability to unknown pathogens, and compactness. SASHA encompasses automatic fault recognition and response over a wide range of possible faults. Moreover, it is an adaptive architecture that can learn and evolve its monitoring and inference capabilities over time to deal with unknown faults. We illustrate the workings of SASHA using the example of fault-tolerant sensor data collection and outline an agenda for future research.

SASHA: toward a self-healing hybrid sensor network architecture

This paper reports on the results of a simulation comparison made by an independent researcher using the ns-2.26 simulator for the WSN protocols: directed diffusion (DD), two-tier data dissemination (TTDD), and gradient broadcast (GRAB). Our performance study provides useful insights for the network designer - such as which protocols (and design choices) control the traffic well, improve data delivery or reduce overall energy consumption. We observe and identify a number of statically configured parameters for each protocol which reduce performance and suggest how they should be dynamically configured in response to measured network state.

A performance comparison of data dissemination protocols for wireless sensor networks

Target detection and tracking is a well-established area of research. However, a majority of proposed solutions in existing literature rely on expensive and specialized sensors, which often have limited coverage. Using low cost sensor nodes is an attractive and complementary approach to scalable target detection and tracking applications. However, tracking with low cost Wireless Sensor Network (WSN), presents its own challenges, namely real time decision making, high frequency sampling, multi-modal sensing, complex signal processing, and data fusion. In this work, we investigate the use of inexpensive off-the-shelf WSN devices for ground surveillance. Our system estimates and tracks a target based on the spatial differences of the target object's signal strength detected by the monitoring sensors at different locations.

Detection and tracking using wireless sensor networks

Ad hoc Wireless Sensor Networks derive much of their promise from their potential for autonomously monitoring remote or physically inaccessible locations [3]. As we begin to deploy sensor networks in real world applications [8], concerns are being raised about the fidelity and integrity of the sensor network data. In this paper, we motivate and propose an online algorithm that leverages Competitive Learning Neural Network for characterization of a dynamic, unknown environment. Based on the proposed characterization sensor networks can autonomously construct multimodal views of their environments and derive the conditions for verifying data integrity over time.

Tatiana Bokareva

Papers

Wireless Sensor Networks for Battlefield Surveillance

SASHA: toward a self-healing hybrid sensor network architecture

A performance comparison of data dissemination protocols for wireless sensor networks

Detection and tracking using wireless sensor networks

Learning Sensor Data Characteristics in Unknown Environments