Showing papers presented at "International Conference on Computer Communications and Networks in 2015"

WiG: WiFi-Based Gesture Recognition System

Abstract: Most recently, gesture recognition has increasingly attracted intense academic and industrial interest due to its various applications in daily life, such as home automation, mobile games. Present approaches for gesture recognition, mainly including vision-based, sensor-based and RF-based, all have certain limitations which hinder their practical use in some scenarios. For example, the vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. To address these, we propose WiG in this paper, a device-free gesture recognition system based solely on Commercial Off-The-Shelf (COTS) WiFi infrastructures and devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. We implemented WiG in indoor environment and conducted experiments to evaluate its performance in two typical scenarios. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario.

Investigating Security and Privacy of a Cloud-Based Wireless IP Camera: NetCam

Abstract: Over the last few years, there has been a significant increase in the number of IP cameras used in various places including markets, malls, pharmacies, movie theatres and schools. Recent products on the market are cloud-based and upload the captured video to a cloud server. With widespread use, security of these IP cameras emerges as an important issue. However, there is still very little work done on the security of these devices. In this paper, we investigate security of cloud-based wireless IP cameras. Security of these devices spans multiple research areas including secure multimedia, network security and cloud security. We have investigated the traffic generated by a low-end, easy-to-setup, off-the-shelf wireless IP camera for average home user. We explored the security precautions taken by the manufacturers of IP cameras and evaluated the access control mechanisms in place. We used a variety of open source and non-commercial tools in our investigation. We identified many security and privacy issues in using these devices ranging from minor to severe issues.We showed that if a malicious person can sniff the IP camera's network traffic anywhere in between the mobile device-cloud servers-IP camera path, he would be able to reconstruct the JPEG images, which is a serious a privacy issue.

To NACK or Not to NACK? Negative Acknowledgments in Information-Centric Networking

Abstract: Information-Centric Networking (ICN) is an internetworking paradigm that offers an alternative to the current IP-based Internet architecture. ICN's most distinguishing feature is its emphasis on information (content) instead of communication endpoints. One important open issue in ICN is whether negative acknowledgments (NACKs) at the network layer are useful for notifying downstream nodes about forwarding failures, or requests for incorrect or non-existent information. In benign settings, NACKs are beneficial for ICN architectures, such as CCNx and NDN, since they flush state in routers and notify consumers. In terms of security, NACKs seem useful as they can help mitigating so-called Interest Flooding attacks. However, as we show in this paper, network-layer NACKs also have some unpleasant security implications. We consider several types of NACKs and discuss their security design requirements and implications. We also demonstrate that providing secure NACKs triggers the threat of producer-bound flooding attacks. Although we discuss some potential countermeasures to these attacks, the main conclusion of this paper is that network-layer NACKs are best avoided, at least for security reasons.

Optimal Deployment of Wireless Sensor Networks for Air Pollution Monitoring

Abstract: Recently, air pollution monitoring emerges as a main service of smart cities because of the increasing industrialization and the massive urbanization. Wireless sensor networks (WSN) are a suitable technology for this purpose thanks to their substantial benefits including low cost and autonomy. Minimizing the deployment cost is one of the major challenges in WSN design, therefore sensors positions have to be carefully determined. In this paper, we propose two integer linear programming formulations based on real pollutants dispersion modeling to deal with the minimum cost WSN deployment for air pollution monitoring. We illustrate the concept by applying our models on real world data, namely the Nottingham City street lights. We compare the two models in terms of execution time and show that the second flow based formulation is much better. We finally conduct extensive simulations to study the impact of some parameters and derive some guidelines for efficient WSN deployment for air pollution monitoring.

Security-Aware Resource Allocation for Mobile Cloud Computing Systems

Abstract: In this paper, a novel resource allocation algorithm is proposed for secure mobile cloud computing systems. The mobile request for using cloud resource is classified according to its level of security requirement and the amount of required resource for remote computing. We formulate the resource allocation problem as a semi-Markov decision process under the average reward criterion, where the average reward of states is expected to be optimized. Through maximizing the long-term reward while meeting the system requirements of the blocking probability and the amount of resource requested with a security guarantee, the optimal resource allocation policy is calculated by using the linear programming. Simulation results demonstrate that the system adaptively modifies the resource allocation policy for cloud computing, and determines whether to utilize extra resource for security implementation according to the mobile request type, the current traffic, and the cloud resource availability.

Task Allocation for Mobile Cloud Computing in Heterogeneous Wireless Networks

Abstract: The ubiquity of mobile devices creates a rapidly growing market for mobile applications. Many of these applications involve complex processing tasks that are difficult to run on resource constrained mobile devices. This leads to the emergence of mobile cloud computing, in which cloud-based resources are used to enhance the computing capabilities of mobile devices. In this paper, we consider heterogeneous wireless networks in which multiple resource-rich computing nodes can be used as mobile clouds, and mobile devices can upload computation extensive tasks to these mobile clouds. The goal is to minimize the average task response time through determining whether to upload a task, and to which cloud the task should be uploaded. We formalize this task allocation problem, which is proved to be a NP-hard problem, and propose both offline centralized approach and online distributed approach to address this problem. Simulation results show that our approaches outperform others in terms of task response time in various scenarios.

Impact of Network Performance on Cloud Speech Recognition

Abstract: Interactive real-time communication between people and machine enables innovations in transportation, health care, etc. Using voice or gesture commands improves usability and broad public appeal of such systems. In this paper we experimentally evaluate Google speech recognition and Apple Siri - two of the most popular cloud-based speech recognition systems. Our goal is to evaluate the performance of these systems under different network conditions in terms of command recognition accuracy and round trip delay - two metrics that affect interactive application usability. Our results show that speech recognition systems are affected by loss and jitter, commonly present in cellular and WiFi networks. Finally, we propose and evaluate a network coding transport solution to improve the quality of voice transmission to cloud-based speech recognition systems. Experiments show that our approach improves the accuracy and delay of cloud speech recognizers under different loss and jitter values.

Efficient Location Privacy for Moving Clients in Database-Driven Dynamic Spectrum Access

Abstract: Dynamic spectrum access (DSA) is envisioned as a promising framework for addressing the spectrum shortage caused by the rapid growth of connected wireless devices. In contrast to the legacy fixed spectrum allocation policies, DSA allows license-exempt users to access the licensed spectrum bands when not in use by their respective owners. More specifically, in the database-driven DSA model, mobile users issue location-based queries to a white-space database, in order to identify idle channels in their area. To preserve location privacy, existing solutions suggest the use of private information retrieval (PIR) protocols when querying the database. Nevertheless, these methods are not communication efficient and fail to take into account user mobility. In this paper, we address these shortcomings and propose an efficient privacy-preserving protocol based on the Hilbert space filling curve. We provide optimizations for mobile users that require privacy on-the-fly and users that have full a priori knowledge of their trajectory. Through experimentation with two real life datasets, we show that, compared to the current state-of-the-art protocol, our methods reduce the query response time at the mobile clients by a large factor.

Multi-Authority Attribute Based Encryption Scheme with Revocation

Abstract: Attribute Based Encryption (ABE) scheme can achieve information sharing of one-to-many users, without considering the number of users and the users identity. But, the traditional single Attribute Authority (AA) ABE scheme can hardly meet requirements of different agencies in distributed application environment and it is easy to form the system performance bottlenecks. Based on ciphertext-policy ABE scheme, this paper proposes a multi-authority revocable ABE scheme, where the classification manages user attributes, effectively relieving the management burden of single organization. In addition, it can achieve fine grained access control of shared information by adopting tree access strategy and secret sharing scheme, and support system attribute revocation. Finally, we show that the scheme is secure against chosen plaintext attack under the Decisional Bilinear Diffie-Hellman (DBDH) assumption.

TinySet - An Access Efficient Self Adjusting Bloom Filter Construction

Abstract: -Bloom filters are a very popular and efficient data structure for approximate set membership queries. However, Bloom filters have several key limitations as they require 44% more space than the lower bound, their operations access multiple memory words and they do not support removals. This work presents TinySet, an alternative Bloom filter construction that is more space efficient than Bloom filters for false positive rates smaller than 2.8%, accesses only a single memory word and partially supports removals. TinySet is mathematically analyzed and extensively tested and is shown to be fast and more space efficient than a variety of Bloom filter variants. TinySet also has low sensitivity to configuration parameters and is therefore more flexible than a Bloom filter.

On Effectiveness of Smart Grid Applications Using Co-Simulation

Abstract: The smart grid is a complex system that comprises components from both the power grid and communication networks To understand the behavior of such a complex system, co-simulation is a viable tool to capture the interaction and the reciprocal effects between a communication network and a physical power grid In this paper, we systematically review the existing efforts of co-simulation and design a framework to explore co-simulation scenarios Using the demand response and energy price as examples of smart grid applications and operating the communication network under various conditions (eg, normal operation, performance degrade, and security threats), we implement these scenarios and conduct a performance evaluation of smart grid applications by leveraging a co-simulation platform

Toward Latency-Aware Dynamic Middlebox Scheduling

Abstract: Under the current Internet environment, middlebox management has become a significant challenge for network operators. Schemes in prior works tried to simplify it with Software-Defined Networking (SDN) technologies, and they provided reliable and flexible approaches to configure the middlebox-related flow entries. However, these schemes are inefficient in resource utilization with the dynamically changing traffic requirements, as they mainly focus on stationary hardware middleboxes. Further-more, latencies of packets lack controls under these schemes. In this paper, inspired by Network Function Virtualization (NFV), we employ software middleboxes, and build a processing delay model to formulate latency behaviours. And based on this model, we present a latency-aware NFV/SDN scheme called Quokka with portable software-based middleboxes that can be dynamically scheduled (placed) according to the changing traffic. Quokka controls the number of middleboxes by efficient and automatic scheduling of both traffic and middlebox positions, and thus reduces the transmission latencies of the network. Comprehensive experiments show: 1) compared with traditional configuration methods, Quokka reduces the transmission delay by about 20% on average; 2) Quokka requires 30% to 50% less middleboxes than traditional schemes to achieve the same performance.

Adaptive Monitoring for Mobile Networks in Challenging Environments

Abstract: The increasing capabilities of mobile communication devices are changing the way people interconnect today. Similar trends in the communication technology domain are leading to the expectation that data and media are available anytime and everywhere. A result is an increasing load on communication networks. In dynamic mobile networks that particularly rely on wireless communication such data requirements paired with environmental conditions like mobility or node density increase the risk of network failure. Consequently, monitoring is crucial in mobile networks to ensure reliable and efficient operation. Current monitoring mechanisms mostly rely on a static architecture and exhibit problems to handle the changes of mobile networks and environmental conditions over time. In this paper, an adaptive monitoring mechanism is presented to overcome these limitations. The mechanism exploits the connectivity and resource characteristics of mobile communication devices to (i) reconfigure its monitoring topology and (ii) adapt to changes of mobile networks and environmental conditions. Through evaluations we show that our proposed solution reduces the achieved relative monitoring error by a factor of six and represents a robust and reliable monitoring mechanism for these challenging environments.

Network-Aware Instance Scheduling in OpenStack

Abstract: Cloud computing systems require a placement logic that decides where to allocate resources. In state-of-the-art platforms such as OpenStack, this scheduler takes into account multiple constraints when starting a new instance, including in particular the required computational and memory resources. However, this scheduling mechanism typically neither considers network requirements of Virtual Machines nor the networking resources that are actually available. In this paper we present an extension of the OpenStack scheduler that enables a network-aware placement of instances by taking into account bandwidth constraints to and from nodes. Our solution keeps track of host-local network resource allocation, and it can be combined with bandwidth enforcement mechanisms such as rate limiting. We present a prototype that requires only very few changes in the OpenStack open source software. Testbed measurement results demonstrate the benefit of our solution compared to the OpenStack default approach.

Faster Web through Client-Assisted CDN Server Selection

Abstract: Modern websites use Content Delivery Networks (CDNs) to speed up the delivery of static content. However, we show that DNS-based selection of CDN servers can be refined to fully deliver on the speedup of CDNs. We propose DNS-Proxy (dp), a client-side process that shares load-balancing functionality with CDNs by choosing from among resolved CDN servers based on last mile network performance. Our measurement study of CDN infrastructure deployed by five major CDN providers shows that dp reduces webpage load time by 29% on average. If dp has already resolved the domain, the reduction in webpage load time is as much as 40%. Finally, dp reduces the load time of individual static Web objects by as much as 43%. We argue that dp enables a more effective use of existing content delivery infrastructure and represents a complementary strategy to a continual increase of geographic content availability.

Clustered Spatio-Temporal Compression Design for Wireless Sensor Networks

Abstract: Since the temporal and spatial correlations of sensor readings are existent in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC) and compressed sensing (CS) for correlated data. The proper selections of NC coefficients and measurement matrix are designed for this scheme. This design guarantees the reconstruction of clustered compression data successfully with an overwhelming probability and unifies the operations of NC and CS into real field successfully. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing the independent encoding in each sensor node (including the cluster head nodes) and joint decoding in sink node. At the same time it has lower computational complexity as compared with JSM-based spatio-temporal scheme by exploiting the temporal and spatial correlations of original sensing data step by step. Finally, the simulation results verify that the clustered spatio-temporal compression scheme outperforms the other two compression schemes significantly in terms of recovery error and compression gain.

Comparison of OXC Node Architectures for WDM and Flex-Grid Optical Networks

Abstract: Large scale optical cross-connects (OXCs) are required due to the increasing traffic demands. Currently, wavelength-selective switches (WSS) are utilized to create the OXCs. However, the port count of commercially available WSSs is limited. To achieve high port counts in OXCs, the existing WSS-based approach is to cascade WSSs, which results in a square order increment in the number of required WSSs. To save the hardware costs in terms of number of WSSs, two novel OXC architectures utilizing the waveband switching technique have been proposed. In this paper, we conduct a detailed comparison among the conventional cascading architecture and the two new architectures. We propose algorithms to accommodate dynamic traffic demands for all architectures and compare the blocking rates. The results show that the blocking rates of the novel architectures are very small and close to that of the cascading architecture, while the novel architectures have much less node complexity in terms of hardware requirement.

SocialQ&A: An Online Social Network Based Question and Answer System

Abstract: Question and Answer (Q&A) systems play a vital role in our daily life for information and knowledge sharing. Users post questions and pick questions to answer in the system. Due to the rapidly growing user population and the number of questions, it is unlikely for a user to stumble upon a question by chance that (s)he can answer. Also, altruism does not encourage all users to provide answers, not to mention high quality answers with a short answer wait time. The primary objective of this paper is to improve the performance of Q&A systems by actively forwarding questions to users who are capable and willing to answer the questions. To this end, we have designed and implemented SocialQ&A, an online social network based Q&A system. SocialQ&A leverages the social network properties of common-interest and mutual-trust friend relationship to identify an asker's friends who are most likely to answer the question. We describe the architecture, algorithms and user interface of SocialQ&A, and analyze the Q&A behavior of real users and questions from a small-scale real-world SocialQ&A system. We also conducted comprehensive large-scale simulation to evaluate SocialQ&A in comparison with other methods. Our results suggest that social networks can be leveraged to improve the answer quality and asker's waiting time.

Exploiting Active Sub-Areas for Multi-Copy Routing in VDTNs

Abstract: In Vehicle Delay Tolerant Networks (VDTNs), current routing algorithms select relay vehicles based on either vehicle encounter history or predicted future locations. The former method may fail to find relays that can encounter the target vehicle in a large-scale VDTN while the latter method may not provide accurate location prediction due to traffic variance. Therefore, these methods cannot achieve high performance in terms of routing success rate and delay. In this paper, we aim to improve the routing performance in VDTNs. We first analyze vehicle network traces and observe that i) each vehicle has only a few active sub-areas that it frequently visits, and ii) two frequently encountered vehicles usually encounter each other in their active sub-areas. We then propose Active Area based Routing method (AAR) which consists of two steps based on the two observations correspondingly. AAR first distributes a packet copy to each active sub-area of the target vehicle using a traffic-considered shortest path spreading algorithm, and then in each sub-area, each packet carrier tries to forward the packet to a vehicle that has high encounter frequency with the target vehicle. Extensive trace-driven simulation demonstrates that AAR produces higher success rates and shorter delay in comparison with the state-of-the-art routing algorithms in VDTNs.

NDN Live Video Broadcasting over Wireless LAN

Abstract: Named Data Networking (NDN) is a new Internet architecture that replaces today's focus on where - addresses and hosts - with what - the content that users and applications care about. One of NDN's prominent advantages is scalable and efficient content distribution due to its native support of caching and multicast in the network. However, at the last hop to wireless users, often the WiFi link, current NDN implementation still treats the communication as multiple unicast sessions, which will cause duplicate packets and waste of bandwidth when multiple users request for the same popular content. WiFi's built-in broadcast mechanism can alleviate this problem, but it suffers from packet loss since there is no MAC-layer acknowledgement as in unicast. In this paper, we develop a new NDN-based cross-layer approach called NLB for efficient and scalable live video streaming over wireless LAN. The idea is to use WiFi's broadcast channel to deliver content from the access point to the users, a leader-based mechanism to suppress duplicate requests from users, and receiver-driven rate control and loss recovery. The design is implemented and evaluated in a physical testbed comprised of a commodity residential access point and 20 WiFi clients. While NDN with multiple unicast sessions or plain broadcast can support no more than 7 concurrent viewers of a 1Mbps streaming video, NDN plus NLB supports all 20 viewers, and can likely support many more when present.

Coalition Formation Towards Energy-Efficient Collaborative Mobile Computing

Abstract: With mobile offloading, computation-intensive tasks can be offloaded from mobile devices to the cloud to conserve energy. In principle, the idea is to trade the relatively low communication energy expense for high computation power consumption. In this paper, we propose that computation-intensive tasks can be distributed among nearby mobile devices, and focus on the case that a group of mobile users may collaborate with one another with one common target job. In particular, a user can reduce its own energy consumption by delegating a portion of the job to nearby users in a coalition. We propose distributed collaboration strategies based on game theory, and formulate the problem as a non-transferable utility coalition formation game in which users join or split from coalitions depending on the local preference. The stability of the resulting partition is studied. We show through simulation that the proposed algorithm reduces up to 22% of the average energy costs compared to the non-cooperative case, and the running time scales well as the number of users grows.

OpenRouteFlow: Enable Legacy Router as a Software-Defined Routing Service for Hybrid SDN

Abstract: Hybrid SDN is an important direction for the evolution of SDN. It is one of the challenges to be addressed for hybrid SDN to open the legacy router and enable the routing view as a software defined routing service. In this paper, OpenRouteFlow is presented, which opens legacy routing protocols and flow sampling information as a routing view service for network applications by software update. OpenRouteFlow tries to realize the decoupling of network visualization and network control. It provides path-oriented and traffic-oriented subscription and publication services for different scenarios of network control. In a hybrid network consisted of OpenFlow switches and legacy routers, OpenRouteFlow is able to map OpenFlow control rules into ACL or RIB in order to support flexible software defined capacities in legacy routers.

Mynah: Enabling Lightweight Data Plane Authentication for SDN Controllers

Abstract: Software defined networking (SDN) has created new opportunities for both the research community and the net-working industry, but SDN also has created new challenges. One of the most fundamental SDN security issues we have ob-served is the absence of data plane authentication. We present a definition of this problem and a controller and switch design called Mynah that makes a first step towards addressing this problem. We then present a prototype experiment results and show that Mynah solves the issue very effectively with a trivial overhead around 4.5 percent of overall communication latency.

Truthful Auction for Resource Allocation in Cooperative Cognitive Radio Networks

Abstract: Cooperative cognitive radio network (CCRN) is a promising paradigm to increase spectrum utilization and exploit spatial diversity. The allocation of two related resources, i.e. spectrum and relay nodes, plays a fundamental role in the performance of CCRNs. However, previous works either lack of incentives for both primary users (PUs) and relay nodes to participate in or consider spectrum auction and relay auction separately. In this paper, we consider a static cooperative cognitive radio network scenario with several PUs and multiple secondary user coteries, each of which consists of a set of secondary users who are interested in sharing the same secondary relay node. We model the problem of joint spectrum allocation and relay allocation as a hierarchical auction and propose TERA, which is the first Truthful auction mechanism for Efficient Resource Allocation in CCRNs. We show that TERA satisfies critical economic properties such as truthful, individual rationality, budget balance, supply limits and computational efficiency. Furthermore, we theoretically prove TERA can achieve near-optimal revenue with high probability. Finally, extensive simulation results show that TERA is efficient and able to improve the utility of PUs and relay nodes significantly up to 125% and 151% respectively.

A Robust Diffusion Adaptive Network Based on the Maximum Correntropy Criterion

Abstract: Adaptive estimation over distributed networks has received a lot of attention due to its broad range of applications. A useful estimation strategy is diffusion adaptive network, where the parameters of interest can be well estimated from noisy measurements through diffusion cooperation between nodes. The conventional diffusion algorithms exhibit good performance in the presence of Gaussian noise but their performance decreases in presence of impulsive noise. The aim of the present paper is to propose a robust diffusion based algorithm that alleviates the effect of impulsive noise. To this end, we move beyond mean squared error (MSE) criterion and recast the estimation problem in terms of the maximum correntropy criterion (MCC). We use stochastic gradient ascent and useful approximations to derive an adaptive algorithm which is appropriate for distributed implementation. The resultant algorithm has the computational simplicity of the popular LMS algorithm, along with the robustness that is obtained by using higher order moments. We present some simulations results which show that the proposed algorithm outperforms existing alternative that rely MSE criterion.

A Framework for Security-Aware Virtual Network Embedding

Abstract: Network virtualization promises a future Internet that inherently supports technological advancement. Extensive literature studies have been devoted to the fundamental problem, namely Virtual Network Embedding (VNE) in network virtualization, which instantiates the virtual network request (VNR) customized by the Service Provider (SP) to the substrate network managed by the Infrastructure Provider (InP). Different from prior studies, in this work, we identify security issues that are associated with the VNE process, which is further incorporated and addressed in the Security-Aware Virtual Network Embedding (SVNE) problem. Our efforts aim to fill the major gaps in the literature studies on the VNE problem: first, we assess the security issues in VNE at node, topology and network levels and explicitly define open, flexible and fine-granular security plans that can address above issues; second, we present and evaluate a comprehensive framework that implements the proposed security plans and address the SVNE problem.

Software Defined Network Inference with Passive/Active Evolutionary-Optimal pRobing (SNIPER)

Abstract: A key requirement for network management is the accurate and reliable monitoring of relevant network characteristics. In today's large-scale networks, this is a challenging task due to the hard constraints of network measurement resources. This paper proposes a new framework, SNIPER, which leverages the flexibility provided by Software-Defined Networking (SDN) to design the optimal observation or measurement matrix that can leads to the best achievable estimation accuracy using Matrix Completion (MC) techniques. To cope with the complexity of designing large-scale optimal observation matrices, we use the Evolutionary Optimization Algorithms (EOA) which directly target the ultimate estimation accuracy as the optimization objective function. We evaluate the performance of SNIPER using both synthetic and real network measurement traces from different network topologies and by considering two main applications including per-flow size and delay estimations. Our results show that SNIPER can be applied to a variety of network performance measurements under hard resource constraints. For example, by measuring 8.8\% of per-flow path delays in Harvard network, congested paths can be detected with probability 0.94. To demonstrate the feasibility of our framework, we also have implemented a prototype of SNIPER in Mininet.

Primary Secrecy Is Achievable: Optimal Secrecy Rate in Overlay CRNs with an Energy Harvesting Secondary Transmitter

Abstract: To tackle the challenging secrecy communication problem in energy harvesting cognitive radio networks, this paper considers an overlay system with one energy harvesting secondary user (SU) to assist primary transmission under the assumption that the primary channel at primary receiver is worse than the eavesdropper. Under such scenario, we optimize the secrecy rate of the PU transmitter by jointly investigating energy harvesting slot, cooperative transmission slot and so on. Given the transmission rate requirement between SUs, the optimization problem is formulated as a mixed integer non-linear (MINLP) program. Due to the special features, we design a polynomial time algorithm SRMA to optimally solve this problem. The algorithm computes the lower bound and upper bound of the transmission power in a secondary transmitter, which are relative with the QoS requirement and energy harvesting parameters. Then SRMA determines its optimal transmission power by iteratively searching between two bounds. Numerical results demonstrate that the primary secrecy rate grows with the increasing energy save ratio and optimal energy save ratio is inversely proportional to the energy harvesting rate.

parkITsmart: Minimization of Cruising for Parking

Abstract: Finding a parking space in urban areas is a daily challenge for drivers across the world, due to the increasing amount of vehicles and the limited amount of parking spaces. Drivers who are looking for a parking space in peak hours are often forced to drive around city blocks until they spot a free parking space. This process is termed in literature "cruising for parking" and is proven to (a) cost a lot of time and gas for drivers, (b) generate unnecessary traffic load, and (c) affect the environment negatively due to increased vehicle emissions. This work proposes a Parking Monitoring and Management System (PMMS) that collects, processes, and presents data about available parking spaces and their tariffs within a geographical region. The end-user application of the PMMS, parkITsmart, delivers at drivers bird's-eye view concerning the parking availability. To facilitate this, the PMMS gathers data from drivers', vehicles, their mobile phones, and Parking Inspectors (PIs). This work shows that in the Internet-of-Things (IoT) environment, "pairing" cars and drivers' mobile phones, collecting data from their sensors, and from PIs in a parking monitoring and management system, can decrease significantly cruising times for parking and can increase the time demands of the parking controlling process.

Design and Implementation of an Autonomous Wireless Sensor-Based Smart Home

Abstract: The Smart home has gained widespread attention due to its flexible integration into everyday life. This next generation green home system, transparently unifies various home appliances, smart sensors and wireless communication technologies. It can integrate diversified physical sensed information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. Although smart homes are gaining popularity due to their energy saving and better living benefits, there is no standardized design for smart homes. In this paper, we put forward a concept by designing and implementing a smart home system which can classify and predict the state of the home based on historical data. We set up a wireless sensor network and collected months of data. By employing supervised machine learning technique, we were able to establish patterns and use the acquired information as a vital cog in our control system algorithm, thereby improving the intelligence of the home. We created a system capable of running with minimal human supervision, an attribute which makes our system an asset for senior care scenarios. Our system also caters to the safety of the home owner.