The rapid expansion of IoT devices and their real-time applications have driven a growing need for edge computing. To meet this need, efficient and secure solutions are required for running such applications on resource-constrained devices with limited power, CPU, and memory. Unikernel, with its minimalistic design and application-specific approach, offers a promising alternative to traditional virtualization and container technologies in these environments. The existing research does not thoroughly examine the feasibility of using unikernel for edge computing. This paper investigates the potential of unikernel for ARM-powered edge computing by evaluating the performance and efficiency of three prominent unikernel systems such as OSv, Nanos, and Unikraft against Docker container. We experiment with real-world edge computing applications and utilize key metrics such as boot time, execution time, memory usage, CPU overhead, and network performance to determine how unikernel performs under the constraints of edge devices. Our findings reveal the potential advantages of unikernel in terms of reduced resource consumption and faster startup times while highlighting areas where they may need further optimization for edge deployment. This study provides valuable insights for researchers and practitioners considering unikernel as a lightweight, efficient solution for edge computing on ARM architectures.

Container orchestration provides the tools necessary to manage the complexities of edge computing. They ensure efficient, reliable, secure deployment and operation of containerized applications. The orchestrators are also pivotal in influencing the quality and cost-efficiency of service deployment by managing essential tasks like container provisioning, scheduling, scaling, and networking. Edge computing environments are resource-constrained, with limited CPU, memory, and network bandwidth compared to traditional cloud environments. Benchmarking helps identify which container orchestration frameworks perform best under these constraints, ensuring optimal resource utilization and performance. Therefore, a thorough benchmarking of these frameworks is crucial. Despite their importance, there is limited work on benchmarking container orchestration in real-world edge-computing scenarios. This paper introduces a systematic approach that facilitates benchmarking container orchestrators using real-world applications. We benchmark Docker Swarm, K3s, KubeEdge, and Nomad orchestrators using computer vision and data science applications. We designed an evaluation framework to assess container orchestration tools. The evaluation framework includes container provisioning time, receiving time, processing time, resource utilization, and throughput metrics. Additionally, we explore how various container orchestrations optimize resource utilization and compare their efficiency on ARM-based edge devices. The benchmark results indicate that the Nomad orchestrator excels in terms of lower latency and optimal resource utilization. KubeEdge and K3s demonstrate faster provisioning times, whereas Docker Swarm experiences communication overhead between manager and worker nodes.

Beacon evaluates whether an intrinsically interpretable rule learner, Information Lattice Learning (ILL) [1], can offer practical advantages for edge AIoT landuse mapping when trained on the same, leakage-safe, physically grounded features as a tuned Random Forest (RF) [2]. Using stratified learning-curve experiments (10–125 samples per class; five seeds; fivefold CV) across five algorithmic families, XGBoost and Random Forest achieved top performance while ILL produced compact, ecologically coherent rulebooks at a 15–25 percentage-point interpretability tax suitable for audit and expert scrutiny. We summarize the end-to-end pipeline, report statistically rigorous results (Wilson CIs, paired tests with Bonferroni control, Cohen's d), and discuss deployment patterns that pair RF's accuracy with ILL's transparency for on-device audit and rapid expert contestability. Our conclusions are bounded by the Costa Rica domain, fixed hyperparameters, and a single ILL implementation; ongoing work targets expert rule validation, sensitivity analyses, and multitemporal/multimodal inputs. These claims and numbers are drawn from the full Beacon study.

Container-based applications are increasingly favored for their efficiency in software development, deployment, and operation across various platforms. However, the growing number of security and privacy attacks poses significant concerns. Exploiting vulnerabilities within containers may compromise the entire host system, as both share the same operating system. Unfortunately, container defense mechanisms are inadequate due to the ever-evolving and dynamic attack landscape.In this paper, we systematize container attacks and defense mechanisms. We systematically analyze the effectiveness of (i) static container scanning tools proposed for vulnerability detection and reveal their shortcomings, as well as (ii) existing run-time anomaly-based detection approaches. We then establish an evaluation framework and comprehensively re-evaluate cutting-edge anomaly detection techniques tailored for containers using an extensive dataset of 51 real-world vulnerabilities. We emphasize that existing defenses are ineffective in protecting containers against state-of-the-art attacks. While anomaly detection-based approaches show potential in addressing dynamic attack landscapes, their high false positive rates and limited training data hinder practicality. Therefore, our work highlights the urgent need for further research to enhance the security of container-based applications.

Containers provide a better performance, faster deployment than virtual machines and provide near-native performance, with isolation and security drawbacks. Although the security of containers for the Intel architecture has been investigated in more detail, there is limited work on the security of containers for the ARM architecture. In this paper, we address this gap in research and focus on the security of containers designed for the ARM architecture, which is heavily used in IoT devices. Edge computing offers many advantages, including reduced latency and resource requirements at the cloud because data can be processed at the edge before it is sent to the cloud. Using containers at the edge nodes of IoT-Edge-Cloud systems can enhance such advantages at the cost of increasing security vulnerabilities in such systems. Therefore, it is essential to investigate the security of containers designed for the ARM architecture. Accordingly, we obtained official ARM images from DockerHub and used various security tools to scan these ARM images. We found that 72% of all the vulnerabilities show varying severity levels and each tool seems to work best for particular base images. We investigated how each tool detects sub-packages and achieves a different hit ratio while none of them alone can detect at least 80% of all the vulnerabilities. In addition, we also investigated how the Docker images and their vulnerability landscape change over a period of six months by running the scanning tools twice. Finally, we also conducted a dynamic analysis of some of the images on the Raspberry Pi and study their effects. We believe this paper will facilitate the use of ARM containers at the ARM-based edge nodes by addressing security issues.

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises cannot afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives.

Unexpected downtime and sudden breakdown of IoT devices can be extremely destructive most especially for safety-critical systems. Condition monitoring and health state estimation are vital techniques for maintaining high reliability and availability. Many data-driven approaches to Remaining Useful Life (RUL) estimation using deep learning algorithms are computationally-intensive and often not feasible on resource-constrained devices. Time lag and network cost associated with massive data transfer to a centralized cloud for processing can be minimized by adopting edge computing. In this paper, we propose a lightweight Long Short-Term Memory (LSTM) based model called LiRUL, suitable for RUL estimation on an edge device. We also implement a tagging function and evaluate our approach on publicly available Turbofan Engine datasets.

In this current era of Internet of Things, data privacy and security of internet enabled devices has become a major concern of many users and manufacturers. Massive amount of data is being generated by these IoT devices and there might be possibilities of user's information being exposed without any privacy protection. The rate of data transfer, size, kind of information transmitted and secure channels used by these IoT devices are of utmost importance and demand more exploratory research. It is not all IoT devices that utilize encryption in their data transmission and those devices that incorporate such security measure can be compromised by the interception of generated traffic via proxy server and its decryption. In this paper, we explore and investigate the data being transmitted by six representative IoT devices and analyze the data, using a proxy server to capture both HTTP and HTTPS traffic. Our results show that one of the IoT devices transmit data in plain text while others utilize encryption. User's information, MAC address and IP address were identified in our data analysis. We propose that IoT devices should not allow proxy connections and implement machine learning algorithms to detect proxies using network connection information.

Through time inference attacks, adversaries fingerprint SDN controllers, estimate switches flow-table size, and perform flow state reconnaissance. In fact, timing a SDN and analyzing its results can expose information which later empowers SDN resource-consumption or saturation attacks. In the real world, however, launching such attacks is not easy. This is due to some challenges attackers may encounter while attacking an actual SDN deployment. These challenges, which are not addressed adequately in the related literature, are investigated in this paper. Accordingly, practical solutions to mitigate such attacks are also proposed. Discussed challenges are clarified by means of conducting extensive experiments on an actual cloud data center testbed. Moreover, mitigation schemes have been implemented and examined in details. Experimental results show that proposed countermeasures effectively block time inference attacks.

Mobility within Wireless Sensor Networks (WSNs) has been widely considered for data collection tasks, where mobile robots physically collect the data from the sensors and return to the base station. Although this approach has proven to be useful in prolonging the lifetime of these networks, it cannot meet the requirements of real-time data collection tasks. For such tasks, we need to utilize mobile robots to create a connected path from the base station to the event, as well as use in-network forwarding through that path. This will provide a longer lifetime while addressing efficiency and scalability issues because mobile robots have a larger and renewable energy reserve, a longer transmission range, and capacity. One of the fundamental problems is how to coordinate robots to establish a connected path from the event location to the base station. We consider this fundamental problem with two objectives, namely minimizing distance traveled by the robots and minimizing hop count (the number of robots used on paths) under the constraints of satisfying the path and/or network connectivity. After mathematically formulating the underlying problems and discussing their NP-hardness, we propose various heuristic solutions. We then demonstrate the efficacy of our proposed solutions through extensive simulations.

Storage performance bottlenecks are one of the major threats limiting the scalability of I/O intensive applications. Parallel storage systems have the potential to alleviate I/O bottlenecks through concurrent operation of independent storage components if a parallelism-aware data layout can be continuously guaranteed. Existing systems use one-layout-fits-all data placement strategy that frequently results in sub-optimal I/O parallelism. Guided by association rule mining, graph coloring, bin packing, and network flow techniques, this paper proposes a general framework for self-optimizing parallel storage systems, with the goal of continuously providing a high-degree of I/O parallelism that is robust to changes in the parallel access patterns of applications and the coexistence of applications with different parallel access characteristics. Evaluation results indicate that the proposed framework is highly successful in adjusting to skewed parallel access patterns for both traditional hard disk drive (HDD) based storage arrays and solid-state drive (SSD) based all-flash arrays. In addition to the storage arrays, the proposed framework is sufficiently generic to be tailored to various other parallel storage scenarios including but not limited to key-value stores, parallel/distributed file systems, and internal parallelism of SSDs.

With the rapid growth of Internet-of-Things (IoT) devices, security and privacy issues emerged as a potential roadblock for widespread adoption. Preliminary research indicates that many types of IoT devices have serious vulnerabilities. It is not easy to investigate security and privacy issues since each type of device is different and manual experiments need to be conducted on the device. In this paper, we propose a testbed for investigation of security and privacy issues of IoT devices. Fundamental approach used in the testbed is to capture layer 2 and layer 3 packets and to analyze the packets for various features. Proposed testbed is low cost and is based on off-the-shelf hardware and open source software. Using the testbed, we can investigate security and privacy issues of many IoT devices including HDMI sticks, IP cameras, activity trackers, smart watches and drones. A large set of experiments can be conducted on IoT devices using the testbed including vulnerability scans, identifying insecure protocol versions, investigation of firmware updates, authentication issues and privacy violations. Sample experimental results show the promise of the proposed testbed. We believe this testbed will serve as the foundation for a general framework to investigate securityand privacy issues of most IoT devices.

Efficient retrieval of replicated data from multiple disks is a challenging problem, especially for heterogeneous storage architectures. Recently, maximum flow based optimal replica selection algorithms were proposed guaranteeing the minimum retrieval time in heterogeneous environments. Although optimality of the retrieval schedule is an important property, execution time of the replica selection algorithm is also crucial since it might significantly affect the performance of the storage sub-system. Current replica selection mechanisms achieve the optimal response time retrieval schedule by performing multiple runs of a maximum flow algorithm in a black-box manner. Such black-box usage of a maximum flow algorithm results in unnecessary flow calculations since previously calculated flow values cannot be conserved. In addition, most new generation multi-disk storage architectures are powered with multi-core processors motivating the usage of multithreaded replica selection algorithms. In this paper, we propose multithreaded and integrated maximum flow based optimal replica selection algorithms handling heterogeneous storage architectures. Proposed and existing algorithms are evaluated using various homogeneous and heterogeneous multi-disk storage architectures. Experimental results show that proposed sequential integrated algorithm achieves 5X speed-up in homogeneous systems, and proposed multithreaded integrated algorithm achieves 21X speed-up using 16 threads in heterogeneous systems over the existing sequential black-box algorithm.

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.

Mobility within Wireless Sensor Networks (WSNs) has proven to be quite useful in prolonging the lifetime of these networks. This mobility requires intelligent control of the robots in order to efficiently complete tasks within the network. If the tasks required of the network need to send audio-video data back to the base station, then we need to maintain some connectivity with the base station in order to efficiently use the energy within the network. After mathematically formulating our problem, we propose three algorithms to solve these problems and run extensive simulations to test these algorithms. We were able to optimally solve the minimum hop problem and compare that to the heuristics for the minimum distance problems.

Chromecast is a small, system-on-chip device, that plugs into the HDMI port of a larger screen and turns it into a smart screen. It is designed for multimedia streaming in a home-network environment. By setting up Chromecast, you can stream videos onto a larger screen and control it from a mobile device such as a smart-phone, tablet or a laptop. We examined the network packets exchanged between the smaller remote control device and the Chromecast attached larger screen. While Chromecast encrypts most of the content, remote control device sends control packets to the remote servers in the clear-text, which makes it vulnerable to reply-attacks or session-hijacking attacks. Besides, data transmission pattern leak personal information outside of the home-network, raising privacy concerns. Network protocols used by Chromecast are investigated and known vulnerabilities are listed. A method to detect the existence of Chromecast behind a home-router is proposed.

Replicated declustering techniques reduce response times of disk requests by distributing data among multiple disks and retrieving in parallel. Efficient retrieval of replicated data from multiple disks is a challenging problem, especially for heterogeneous storage architectures receiving continuous disk requests. Existing techniques either do not consider the heterogeneity of the disks or handle the requests in a discrete manner assuming the storage system is idle. In this paper, we focus on continuous retrieval techniques in heterogeneous storage architectures to minimize the response time of disk requests considering waiting time and service time of the requests as well as the execution time of the retrieval algorithm. We investigate multiple trade-offs between these three factors affecting the response time of a disk request and propose a maximum flow based adaptive retrieval strategy. Performance of the proposed and existing continuous retrieval techniques are evaluated using simulations driven by real world traces and various homogeneous and heterogeneous multi-disk storage configurations.

Mobile robots improve scalability and performance of wireless sensor networks. Protocols for many services including data collection, localization, topology control and security in wireless sensor networks include mobile robots. Using a single robot limits the scalability and performance and multiple robots are used as a result. When multiple robots are deployed, it is desirable to have the robots connected to provide improved services. However, coordinating the robots optimally to achieve connectivity among them is not trivial. We use computational geometry techniques to achieve connectivity. We use the concept of Frechet distance between curves to synchronize the robots for connectivity. We extend the idea of Frechet distance to multiple curves where each curve is the path of a robot. We analyze the proposed idea theoretically and show that the theory can not be applied directly due to limitations on robot speed and speed changes. Therefore, we propose a practical approach where maximum robot speed is bounded and speed changes is limited. Simulations show that the connectivity among the robots is maintained when the robots follow the movement pattern based on the Frechet distance between the paths of consecutive robots.

Many applications in wireless sensor networks can benefit from position information. However, existing accurate solutions for indoor environments are costly. Radio-Frequency (RF)-based approaches are not suitable for some indoor environments such as factory floors where heavy machinery can cause interference. We propose a low-cost and simple location management system using infrared (IR) leds and the Wii Remote Controller (WRC) which has an IR camera. The proposed solution is motivated by the need to find the location of a mobile robot used for data collection in a wireless sensor network. In the proposed schemes, the WRC is placed vertically on the mobile robot pointing upward and IR leds are placed irregularly on the ceiling. The mobile robot determines its position using the relative positions of the IR leds detected by the WRC. The WRC senses a few IR leds at a time, and they are differentiated using the irregularity among them. We analyze the problem theoretically and show that there exist limitations for covering large areas. We also discuss how to overcome these limitations. For small coverage areas, we provide optimal solutions using linear programming. The proposed scheme uses the resources efficiently and can cover a large area using a single WRC and multiple IR leds. We have simulation results including nonvertical placements of the WRC. The proposed scheme is easy to implement and requires minimal bandwidth for location management.

Mobile elements (MEs) have been used to improve the scalability and the performance of wireless sensor networks (WSNs). In most of these approaches, the ME traverses an area of interest with a pre- determined path set by the basestation (BS) and is trusted to follow the determined path. This assumption is not realistic since the ME carries essential information about the network making it appealing to various attackers. An ME can be captured and can be used to deceive the BS. As a result, further protocols are required to verify the path travelled by the ME. In this paper, we propose two protocols to control the path of a semi-trusted ME using cryptographic methods including Message Authentication Code (MAC), and hash chains. First protocol collects node encounter information with MACs and allows the BS to construct an approximate path using this information. Density of the nodes and the broadcast range play an important role in the quality of the constructed path. Second protocol enforces a strict path using hash chains. The ME helps the sensors verify a hash chain in a distributed manner and deviation from the path results in breaking the chain. We believe these protocols will serve as a first step to a model where the ME has to prove all of its actions to the BS.

Declustering techniques reduce query response times through parallel I/O by distributing data among parallel disks. Recently, replication-based approaches were proposed to further reduce the response time. Efficient retrieval of replicated data from multiple disks is a challenging problem. Existing retrieval techniques are designed for storage arrays with identical disks, having no initial load or network delay. In this article, we consider the generalized retrieval problem of replicated data where the disks in the system might be heterogeneous, the disks may have initial load, and the storage arrays might be located on different sites. We first formulate the generalized retrieval problem using a Linear Programming (LP) model and solve it with mixed integer programming techniques. Next, the generalized retrieval problem is formulated as a more efficient maximum flow problem. We prove that the retrieval schedule returned by the maximum flow technique yields the optimal response time and this result matches the LP solution. We also propose a low-complexity online algorithm for the generalized retrieval problem by not guaranteeing the optimality of the result. Performance of proposed and state of the art retrieval strategies are investigated using various replication schemes, query types, query loads, disk specifications, network delays, and initial loads.

The increasing popularity of the storage cloud is leading organizations to move their applications and enterprise data into the cloud. It is desirable to move time-critical applications demanding high performance I/O operations. Flash based storage arrays have emerged to address the high performance I/O requirements, however, providing predictable Quality of Service (QoS) for applications with real time data requirements is a challenging open problem. This paper introduces a QoS framework for flash based storage arrays. Our framework provides deterministic and statistical response time guarantees through a combination of techniques including replication, data mining, and online retrieval. We evaluated the framework using synthetic and real-world traces. The QoS performance of the system is compared to the existing high-throughput RAID designs. Numerical results show that under the synthetic traces, QoS performance of the proposed system outperforms the existing high performance RAID designs. Real world traces indicate that the proposed QoS mechanism is tunable to support the guarantees required by various real world applications.

Wireless sensor networks are deployed in large numbers to monitor an area of interest. To overcome the scalability issues of large scale wireless sensor networks, mobile robots are introduced. Transmit-only sensors reduce the cost of deployment since they do not need receiver circuit. However, transmit-only sensors can not be used in a homogeneous sensor network since none of the nodes can receive data. In this paper, we propose a framework consisting of transmit-only sensors and a mobile element for data collection. We theoretically analyze the proposed framework, and support it with extensive simulations. We investigate the effect of various system parameters including density of nodes, speed of mobile element, and range of sensors. This work shows that the usage of transmit-only sensors together with a mobile robot is practical.

Efficient retrieval of replicated data from multiple disks is a challenging problem. Traditional retrieval techniques assume that replication is done at a single site using homogeneous disk arrays having no initial load or network delay. Recently, generalized retrieval algorithms are proposed to cover heterogeneous disk arrays, initial loads, and network delays. Generalized retrieval algorithms achieve the optimal response time retrieval schedule by performing multiple runs of a maximum flow algorithm. Since the maximum flow algorithm is used as a black box technique, flow values of the previous runs cannot be conserved to speed up the process. In this paper, we propose integrated maximum flow algorithms for the generalized optimal response time retrieval problem. Our first algorithm uses Ford-Fulkerson method and the second algorithm uses Push-relabel algorithm. Besides the sequential implementations, a multi-threaded version of the push-relabel algorithm is also implemented. Proposed algorithms are investigated using various replication schemes, query types, query loads, disk specifications, and system delays. Experimental results show that the sequential integrated push-relabel algorithm runs up to 2.5X faster than the black box version. Furthermore, parallel integrated push-relabel implementation achieves up to 1.7X speed up (~1.2X on average) over the sequential algorithm using two threads, which makes the integrated algorithm up to 4.25X (~3X on average) faster than its black box counterpart.

Declustering techniques reduce query response times through parallel I/O by distributing data among multiple devices. Except for a few cases, it is not possible to find declustering schemes that are optimal for all spatial range queries. As a result of this, most of the research on declustering have focused on finding schemes with low worst case additive error. Number-theoretic declustering techniques provide low additive error and high threshold. In this paper, we investigate equivalent disk allocations and focus on number-theoretic declustering. Most of the number-theoretic disk allocations are equivalent and provide the same additive error and threshold. Investigation of equivalent allocations simplifies schemes to find allocations with desirable properties. By keeping one of the equivalent disk allocations, we can reduce the complexity of searching for good disk allocations under various criteria such as additive error and threshold. Using proposed scheme, we were able to collect the most extensive experimental results on additive error and threshold in 2, 3, and 4 dimensions.

Many applications in wireless sensor networks (WSNs) demand security. The standart low-power wireless sensors (TmoteSky, MICA2) have severe resource constraints. Despite some work on the adaptation of public-key cryptography to the resource constrained sensors, it is still not obvious whether the resources on the sensors are sufficient for the current asymmetric algorithms run in decent time and space. Therefore, Symmetric cryptography is preferred in most wireless sensor network applications since its resource requirements are lower than the resource requirements of the public-key cryptography. Key management is challenging since sensors can land anywhere after deployment. Earlier approaches on key management mostly focus on key predistribution where a small number of keys are placed in sensors before deployment. In this paper, we propose key distribution schemes using a mobile element. In proposed schemes, mobile robot handles all the overload of key distribution requiring minimal resources at the sensors for key management. Mobile robot broadcasts key messages within a radius, and all the nodes within the area can receive and process the message. We investigate the proposed schemes in detail and show that they are feasible using simulations. Mobile based key distribution can provide a comprehensive key management framework since it can also aid in detection of compromised nodes and do key revocation.

Network Address Translation (NAT) is used for various reasons on the Internet and hides the IP address and number of nodes behind the NAT device. Although many applications benefit from the knowledge of number of active nodes behind a NAT device, existing schemes are limited. In this paper, we use TCP timestamp option to count the number of active nodes. Timestamp option includes current timestamp of the machine in the TCP packet. We propose an efficient scheme that counts the number of machines approximately using clustering of timestamps. We use least-squares line fit of timestamp values and convex hulls to efficiently maintain the crucial information about existing clusters. Proposed scheme is online and requires minimal resources. We have investigated various aspects of the scheme to improve its performance. Using a developed tool to send packets, we have observed that the proposed scheme approximates the number of machines that send more than threshold number of packets well. Real experiments validate the proposed scheme.

Denial of Service (DoS) attacks on a computer system or network cause loss of service to users typically by flooding a victim with many requests or by disrupting the connections between two machines. Although significant amount of work has been done on DoS attacks, DoS attacks on streaming video servers were not investigated in detail. In this paper, we investigate DoS resilience of Real Time Streaming Protocol (RTSP). We show that by using a simple command line tool that opens a large number of RTSP connections, we can launch DoS attacks on the server and the proxy. We discuss in detail how the CPU and the memory resources are affected by the attacks. We observe that, with the DoS attack we launch, clients can also keep the connections alive for a long period and maintain the resources allocated at the server. We propose a lightweight dynamic detection framework for the RTSP based DoS attacks.

Many applications in wireless sensor networks can benefit from position information. However, existing accurate solutions for indoor environments are costly. RF based approaches are not suitable for some indoor environments such as factory floors where heavy machinery can cause interference. In this paper, we propose a low cost and simple location management system using the Wii remote controller and infrared leds. Proposed solution is motivated by the need to find the location of a mobile robot used for data collection in a wireless sensor network. In proposed scheme, Wii remote controller is placed on the mobile robot pointing upward and several IR leds are placed on the ceiling. Proposed scheme uses the resources efficiently and can cover a large area using a single Wii remote controller and multiple IR leds. Proposed scheme is easy to implement and requires minimal bandwith for location management.

Mobile elements, which can traverse the deployment area and convey the observed data from static sensor nodes to a base station, have been introduced for energy efficient data collection in wireless sensor networks (WSNs). However, most existing solutions only plan a single path for the mobile element, which may lead to quick energy depletion for the sensor nodes that are far away from the path. In this paper, for data collection in WSNs, we study the multi-path planning (MPP) problem for the mobile element to prolong the lifetime of WSNs. Observing the intractability of the problem, two MPP heuristic schemes, namely fixed-K and adaptive-K, are proposed. The central idea of these schemes is to plan multiple paths and have the mobile element follow them in turn to balance the energy consumption on individual sensor nodes, thus extending the lifetime of WSNs. The proposed schemes are evaluated through extensive simulations. The results show that, compared to that of the single path solution, the multi-path approaches can extend the lifetime of WSNs by up to four times. Moreover, the adaptive-K scheme treats the sensor nodes more fairly with less variation on their energy consumptions.

Declustering distributes data among parallel disks to reduce retrieval cost using I/O parallelism. Many schemes were proposed for single copy declustering of spatial data. Recently, declustering using replication gained a lot of interest and several schemes with different properties were proposed. It is computationally expensive to verify optimality of replication schemes designed for range queries and existing schemes verify optimality for up to 50 disks. In this article, we propose a novel method to find replicated declustering schemes that render all spatial range queries optimal. The proposed scheme uses threshold based declustering, divisibility of large queries for optimization and optimistic approach to compute maximum flow. The proposed scheme is generic and works for any number of dimensions. Experimental results show that using 3 copies there exist allocations that render all spatial range queries optimal for up to 750 disks in 2 dimensions and with the exception of several values for up to 100 disks in 3 dimensions. The proposed scheme improves search for strictly optimal replicated declustering schemes significantly and will be a valuable tool to answer open problems on replicated declustering.

Video streaming over peer-to-peer networks has received significant attention recently. Accordingly, much work is done on video streaming under the assumption that all the peers have the whole movie. However, it is likely that many peers may only store a fraction of the movie due to their resource constraints. Therefore, there is a need to develop appropriate mechanisms to facilitate efficient streaming when each user stores a fraction of the movie. With this in mind, we propose a distributed data structure called PeerGraph in which each peer determines other peers having the previous or subsequent fractions of the movie and maintains pointers to these peers. In our model, a peer has full control on how many segments it wants to store. However, we require it to store consecutive segments. In order to stream, a client first finds a peer that contains the first segment and then uses the proposed PeerGraph to locate subsequent segments. We use extensive simulations to investigate the effect of various parameters. Simulation results support feasibility of the proposed scheme.

Declustering techniques reduce query response times through parallel I/O by distributing data among multiple devices. Recently, replication based approaches were proposed to further reduce the response time. All of the replication based schemes assume that replication is done at a single site. In this paper, we consider replicated data stored at multiple sites. We formulate multi-site retrieval problem as a maximum flow problem and solve it using maximum flow techniques. We propose a low complexity online algorithm for the problem. We investigate the proposed scheme using various replication schemes, query types and query loads. Proposed scheme can easily be extended to nonuniform data and to any number of sites. Experimental results show that replication using orthogonal allocation performs the best under various settings.

Declustering distributes data among parallel disks to reduce retrieval cost using I/O parallelism. Many schemes were proposed for single copy declustering of spatial data. Recently, declustering using replication gained a lot of interest and several schemes with different properties were proposed. An in-depth comparison of major schemes is necessary to understand replicated declustering better. In this paper, we analyze the proposed schemes, tune some of the parameters and compare them for different query types and under different loads. We propose a three step retrieval algorithm for the compared schemes. For arbitrary queries dependent and partitioned allocation perform poorly, others perform close to each other. For range queries, they perform similarly with the exception of smaller queries in which RDA performs poorly and dependent performs well. For connected queries, partitioned allocation performs poorly and dependent performs well under light load.

The problem of disk declustering is to distribute data among multiple disks to reduce query response times through parallel I/O. A strictly optimal declustering technique is one that achieves optimal parallel I/O for all possible queries. In this paper, we focus on techniques that are optimized for spatial range queries. Current declustering techniques, which have single copies of the data, have been shown to be suboptimal for range queries. The lower bound on extra disk accesses is proved to be Ω(log N) for N disks even in the restricted case of an N-by-N grid, and all current approaches have been trying to achieve this bound. Replication is a well-known and effective solution for several problems in databases, especially for availability and load balancing. In this paper, we explore the idea of replication in the context of declustering and propose a framework where strictly optimal parallel I/O is achievable using a small amount of replication. We provide some theoretical foundations for replicated declustering, e.g., a bound for number of copies for strict optimality on any number of disks, and propose a class of replicated declustering schemes, periodic allocations, which are shown to be strictly optimal. The results for optimal disk allocation are extended for larger number of disks by increasing replication. Our techniques and results are valid for any arbitrary a-by-b grids, and any declustering scheme can be further improved using our replication framework. Using the framework, we perform experiments to identify a strictly optimal disk access schedule for any given arbitrary range query. In addition to the theoretical bounds, we compare the proposed replication based scheme to other existing techniques by performing experiments on real datasets.

Reverse Nearest Neighbor (RNN) queries are of particular interest in a wide range of applications such as decision support systems, profile based marketing, data streaming, document databases, and bioinformatics. The earlier approaches to solve this problem mostly deal with two dimensional data. However most of the above applications inherently involve high dimensions and high dimensional RNN problem is still unexplored. In this paper, we propose an approximate solution to answer RNN queries in high dimensions. Our approach is based on the strong correlation in practice between k-NN and RNN. It works in two phases. In the first phase the k-NN of a query point is found and in the next phase they are further analyzed using a novel type of query Boolean Range Query (BRQ). Experimental results show that BRQ is much more efficient than both NN and range queries, and can be effectively used to answer RNN queries. Performance is further improved by running multiple BRQ simultaneously. The proposed approach can also be used to answer other variants of RNN queries such as RNN of order k, bichromatic RNN, and Matching Query which has many applications of its own. Our technique can efficiently answer NN, RNN, and its variants with approximately same number of I/O as running a NN query.

Similarity based queries are common in several modern database applications, such as multimedia, scientific, and biomedical databases. In most of these systems, database responds with the tuple with the closest match according to some metric. In this paper we investigate some important security issues related to similarity search in databases. We investigate the vulnerability of such systems against users who try to copy the database by sending automated queries. We analyze two models for similarity search, namely reply model and score model. Reply model responds with the tuple with best match and score model responds with only the score of similarity search. For these models we analyze possible ways of attacks and strategies that can be used to detect attacks. Our analysis shows that in score model it is much easier to plug the vulnerabilities than in reply model. Sophisticated attacks can easily be used in reply model and the database is limited in capability to prevent such attacks.

In this paper, we describe error preserving encryption mechanisms for transmission of vido over wireless networks. One of the main problems with the secure transmission of data over wireless networks is that the bit errors that occur need to typically be sesolved before decryption can begin. For vido straming applications, this is unacceptable due to the general requirement that video be presented to the user in a continuous manner with low latency. In this paper, we describe a systematic approach to understanding error preserving encryption algorithms. That is, encryption algorithms designed specifically for video to solve this problem. The main objective of this work is to ensure that the basic encryption of the stream can survive bit errors and that the errors are then passed to the application. We make use of the fact that video compression typically results in random byte distribution. Error preserving encryption algorithms are secure against ciphertext only attacks but vulnerable against known plaintext attacks. We limit this vulnerabillity by requiring a key exchange for each session.

There is a great demand for wireless devices that allow users to freely roam and still access their on-line information. As wireless networking technologies continue to develop, the access to multimedia data such as video will continue to become more viable. Limited bandwidth is a problem when wireless devices are used for video transmission and limited power necessitates power efficient mechanisms. We propose a single retransmission based forward error correction (FEC) scheme that increases the video quality of the video, has efficient power consumption, and performs better in a highly dynamic wireless networks.