A Cooperative Emergency Navigation Framework using Mobile Cloud Computing

The use of wireless sensor networks (WSNs) for emergency navigation systems suffer disadvantages such as limited computing capacity, restricted battery power and high likelihood of malfunction due to the harsh physical environment. By making use of the powerful sensing ability of smart phones, this paper presents a cloud-enabled emergency navigation framework to guide evacuees in a coordinated manner and improve the reliability and resilience in both communication and localization. By using social potential fields (SPF), evacuees form clusters during an evacuation process and are directed to egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm. Rather than just rely on the conventional telecommunications infrastructures, we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong the life time of smart phones, that adaptively searches optimal communication routes between portable devices and the egress node that provides access to a cloud server with respect to the remaining battery power of smart phones and the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by ISCIS 2014 (29th International Symposium on Computer and Information Sciences

Errors and power when communicating with spins

We consider a network composed of a finite set of communicating nodes that send individual particles to each other, and each particle can carry binary information. Though our main motivation is related to communications in nanonetworks with electrons that carry magnetic spin as the bipolar information, one can also imagine that the particles may be molecules that use chirality to convey information. Since it is difficult for a particle to carry an identifier that conveys the identity of the “source” or “destination”, each node receives particles whose source cannot be ascertained since physical imperfections may result in particles being directed to the wrong destination in a manner that interferes with the correctly directed particles, and particles that should arrive at a node may be received by some other node. In addition, noise may randomly switch the polarity of particles, and in the case of magnetic spin we can also have the effect of entanglement.We estimate the error probability in such a multipoint network as a function of the rate of flow of particles, and the power consumption per communicating pair of nodes. We then design a bipolar detector and show that it can significantly eliminate the effect of errors

Mobile network anomaly detection and mitigation: The NEMESYS approach

Mobile malware and mobile network attacks are becoming a significant threat that accompanies the increasing popularity of smart phones and tablets. Thus in this paper we present our research vision that aims to develop a network-based security solution combining analytical modelling, simulation and learning, together with billing and control-plane data, to detect anomalies and attacks, and eliminate or mitigate their effects, as part of the EU FP7 NEMESYS project. These ideas are supplemented with a careful review of the state-of-the-art regarding anomaly detection techniques that mobile network operators may use to protect their infrastructure and secure users against malware

Synchronising energy harvesting and data packets in a wireless sensor

We consider a wireless sensor node that gathers energy through harvesting and reaps data through sensing. The node has a wireless transmitter that sends out a data packet whenever there is at least one “energy packet” and one “data packet”, where an energy packet represents the amount of accumulated energy at the node that can allow the transmission of a data packet. We show that such a system is unstable when both the energy storage space and the data backlog buffer approach infinity, and we obtain the stable stationary solution when both buffers are finite. We then show that if a single energy packet is not sufficient to transmit a data packet, there are conditions under which the system is stable, and we provide the explicit expression for the joint probability distribution of the number of energy and data packets in the system. Since the two flows of energy and data can be viewed as flows that are instantaneously synchronised, this paper also provides a mathematical analysis of a fundamental problem in computer science related to the stability of the “join” synchronisation primitive

Wireless sensor with data and Energy Packets

This paper develops a mathematical model to determine the balance of energy input and data sensing and transmission in a wireless sensing node. Since the node acquires energy through harvesting from an intermittent source, and sensing is also carried out intermittently, the node is modelled with random arrivals of both energy and data. A buffer in the node stores data packets while energy is stored in a battery acting as an energy buffer. The approach uses the “Energy Packet Network” paradigm so that both energy and data packets can be modelled as discrete quantities. We assume that for each data packet, the sensor consumes K e energy packets for node electronics including sensing, processing, and storing and K t energy packets for transmission. We model the node's energy and data flow by a two-dimensional random walk which represents the backlog of data and energy packets. We then simplify the model using companion matrices and matrix algebra techniques that allow us to obtain a closed-form solution for the stationary probability distribution for the random walk which allows us to compute important performance measures, including the energy consumed by the node, and its throughput in data packets transmitted as a function of the amount of power that it receives. The model also allows us to evaluate the effect of ambient noise and the needs for data retransmissions, including for the case where M sensors operate in proximity and create interference for each other

Performance optimization with energy packets

We investigate how the flow of energy and the flow of jobs in a service system can be used to minimize the average response time to jobs that arrive according to random arrival processes at the servers. An interconnected system of workstations and energy storage units that are fed with randomly arriving harvested energy is analyzed by means of the Energy Packet Network (EPN) model. The system state is discretized, and uses discrete units to represent the backlog of jobs at the workstations, and the amount of energy that is available at the energy storage units. An Energy Packet (EP) which is the unit of energy, can be used to process one or more jobs at a workstation, and an EP can also be expended to move a job from one workstation to another one. The system is modeled as a probabilistic network that has a product-form solution for the equilibrium probability distribution of system state. The EPN model is used to solve two problems related to using the flow of energy and jobs in a multi-server system, so as to minimize the average response time experienced by the jobs that arrive at the system

CAM04-1: Admission control in self aware networks

The worldwide growth in broadband access and multimedia traffic has led to an increasing need for Quality- of-Service (QoS) in networks. Real time network applications require a stable, reliable, and predictable network that will guarantee packet delivery under QoS constraints. Network self- awareness through on-line measurement and adaptivity in response to user needs is one way to advance user QoS when overall network conditions can change, while admission control (AC) is an approach that has been commonly used to reduce traffic congestion and to satisfy users' QoS requests. The purpose of this paper is to describe a novel measurement-based admission control algorithm which bases its decision on different QoS metrics that users can specify. The self-observation and self- awareness capabilities of the network are exploited to collect data that allows an AC algorithm to decide whether to admit users based on their QoS needs, and the QoS impact they will have on other users. The approach we propose finds whether feasible paths exist for the projected incoming traffic, and estimates the impact that the newly accepted traffic will have on the QoS of pre-existing connections. The AC decision is then taken based on the outcome of this analysis

Single-Cell Based Random Neural Network for Deep Learning

Recent work demonstrated the value of multi clusters of spiking Random Neural Networks (RNN) with dense soma-to-soma interactions in deep learning. In this paper we go back to the original simpler structure and we investigate the power of single RNN cells for deep learning. First, we consider three approaches with the single cells, twin cells and multi-cell clusters. This first part shows that RNNs with only positive parameter can conduct convolution operations similar to those of the convolutional neural network. We then develop a multi-layer architecture of single cell RNNs (MLSRNN), and show that this architecture achieves comparable or better classification at lower computation cost than conventional deep-learning methods

Nonnegative autoencoder with simplified random neural network

This paper proposes new nonnegative (shallow and multi-layer) autoencoders by combining the spiking Random Neural Network (RNN) model, the network architecture typical used in deep-learning area and the training technique inspired from nonnegative matrix factorization (NMF). The shallow autoencoder is a simplified RNN model, which is then stacked into a multi-layer architecture. The learning algorithm is based on the weight update rules in NMF, subject to the nonnegative probability constraints of the RNN. The autoencoders equipped with this learning algorithm are tested on typical image datasets including the MNIST, Yale face and CIFAR-10 datasets, and also using 16 real-world datasets from different areas. The results obtained through these tests yield the desired high learning and recognition accuracy. Also, numerical simulations of the stochastic spiking behavior of this RNN auto encoder, show that it can be implemented in a highly-distributed manner