Towards a Queueing-Based Framework for In-Network Function Computation

We seek to develop network algorithms for function computation in sensor networks. Specifically, we want dynamic joint aggregation, routing, and scheduling algorithms that have analytically provable performance benefits due to in-network computation as compared to simple data forwarding. To this end, we define a class of functions, the Fully-Multiplexible functions, which includes several functions such as parity, MAX, and k th -order statistics. For such functions we exactly characterize the maximum achievable refresh rate of the network in terms of an underlying graph primitive, the min-mincut. In acyclic wireline networks, we show that the maximum refresh rate is achievable by a simple algorithm that is dynamic, distributed, and only dependent on local information. In the case of wireless networks, we provide a MaxWeight-like algorithm with dynamic flow splitting, which is shown to be throughput-optimal

Cluster-based Routing Protocols for Energy-Efficiency in Wireless Sensor Networks

22 page

Survey on data aggregation based security attacks in wireless sensor network

Wireless sensor network (WSN) has applications in military, health care, environmental monitoring, infrastructure, industrial and commercial applications. The WSN is expected to maintain data integrity in all its network operations. However, due to the nature of wireless connectivity, WSN is prone to various attacks that alter or steal the data exchanged between the nodes. These attacks can disrupt the network processes and also the accuracy of its results. In this survey paper, we have reviewed various attacks available in the literature till date. We have also listed existing methods that focus on data aggregation based security mechanisms in WSN to counter the attacks. We have classified and compared these methods owing to their encryption techniques. This paper intends to support researchers to understand the basic attacks prevalent in WSN and schemes to counter such attacks

Towards Confident Body Sensor Networking

With the recent technology advances of wireless communication and lightweight low-power sensors, Body Sensor Network (BSN) is made possible. More and more researchers are interested in developing numerous novel BSN applications, such as remote health/fitness monitoring, military and sport training, interactive gaming, personal information sharing, and secure authentication. Despite the unstable wireless communication, various confidence requirements are placed on the BSN networking service. This thesis aims to provide Quality of Service (QoS) solutions for BSN communication, in order to achieve the required confidence goals.;We develop communication quality solutions to satisfy confidence requirements from both the communication and application levels, in single and multiple BSNs. First, we build communication QoS, targeting at providing service quality guarantees in terms of throughput and time delay on the communication level. More specifically, considering the heterogeneous BSN platform in a real deployment, we develop a radio-agnostic solution for wireless resource scheduling in the BSN. Second, we provide a QoS solution for both inter- and intra-BSN communications when more than one BSNs are involved. Third, we define application fidelity for two neurometric applications as examples, and bridge a connection between the communication QoS and application QoS

SenMinCom: Pervasive Distributed Dynamic Sensor Data Mining for Effective Commerce

In last few years, the use of wireless sensor networks and cell phones has become ubiquitous; fusing these technologies in the field of business will open up new possibilities. To fill this lacuna, I propose a novel idea where the combination of these will facilitate companies to receive feedback on their products and services. System\u27s unobtrusive sensors will not only collect shopping, mobile usage data from consumers but will also make effective use of this information to increase revenue, cut costs, etc.; the use of mobile agent based data mining allows analyzing the data from different dimensions, categorizing it on factors such as product positioning, promotion of goods, etc. as in the case of a shopping store. Additionally, because of the dynamic mining system the companies get on-the-scene recommendation of products rather than off-the-scene. In this thesis, a novel distributed pervasive mining system is proposed to get dynamic shopping information and mobile device usage of the customers

A Learning-based Approach to Exploiting Sensing Diversity in Performance Critical Sensor Networks

Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting and classifying events while maximizing system lifetime. to meet high accuracy requirements and maximize system lifetime, we must address sensing diversity: sensing capability differences among both heterogeneous and homogeneous sensors in a specific deployment. Existing approaches either ignore sensing diversity entirely and assume all sensors have similar capabilities or attempt to overcome sensing diversity through calibration. Instead, we use machine learning to take advantage of sensing differences among heterogeneous sensors to provide high accuracy and energy savings for performance critical applications.;In this dissertation, we provide five major contributions that exploit the nuances of specific sensor deployments to increase application performance. First, we demonstrate that by using machine learning for event detection, we can explore the sensing capability of a specific deployment and use only the most capable sensors to meet user accuracy requirements. Second, we expand our diversity exploiting approach to detect multiple events using a distributed manner. Third, we address sensing diversity in body sensor networks, providing a practical, user friendly solution for activity recognition. Fourth, we further increase accuracy and energy savings in body sensor networks by sharing sensing resources among neighboring body sensor networks. Lastly, we provide a learning-based approach for forwarding event detection decisions to data sinks in an environment with mobile sensor nodes

Novel Order preserving encryption Scheme for Wireless Sensor Networks

International audienceAn Order-Preserving Encryption (OPE) scheme is a deterministic cipher scheme, whose encryption algorithm produces cipher texts that preserve the numerical ordering of the plain-texts. It is based on strictly increasing functions. It is a kind of homomorphic encryption where the homomorphic operation is order comparison. This means that comparing encrypted data provides the exact result than comparing the original data. It is attractive to be used in databases, especially in cloud ones as a method to enhance security, since it allows applications to perform order queries over encrypted data efficiently (without the need of decrypting the data). Wireless sensor network is another potential domain in which order preserving encryption can be adopted and used with high impact. It can be integrated with secure data aggregation protocols that use comparison operations to aggregate data (MAX, MIN, etc.) in a way that no decryption is being performed on the sensor nodes, which means directly less power consumption. In this paper, we will review many existing order-preserving encryption schemes with their related brief explanation, efficiency level, and security. Then, and based on the comparative table generated, we will propose a novel order-preserving encryption scheme that has a good efficiency level and less complexity, in order to be used in a wireless sensor network with an enhanced level of security

Performance Analysis of a Compression Scheme for Highly Dense Cluster-Based Wireless Sensor Network

International audienc